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Testing video creation

No Subtitle Needed

Pentest Validate 1

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Testing video apollo

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A proper test this time

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McTestment

Test tester

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In the classic cartoon “The Jetsons,” Rosie the robotic maid seamlessly switches from vacuuming the house to cooking dinner to taking out the trash. But in real life, training a general-purpose robot remains a major challenge.Typically, engineers collect data that are specific to a certain robot and task, which they use to train the robot in a controlled environment. However, gathering these data is costly and time-consuming, and the robot will likely struggle to adapt to environments or tasks it hasn’t seen before.To train better general-purpose robots, MIT researchers developed a versatile technique that combines a huge amount of heterogeneous&nbsp;data from many of sources into one system that can teach any robot a wide range of tasks.Their method involves aligning data from varied domains, like simulations and real robots, and multiple modalities, including vision sensors and robotic arm position encoders, into a shared “language” that a generative AI model can process.By combining such an enormous amount of data, this approach can be used to train a robot to perform a variety of tasks without the need to start training it from scratch each time.This method could be faster and less expensive than traditional techniques because it requires far fewer task-specific data. In addition, it outperformed training from scratch by more than 20 percent in simulation and real-world experiments.“In robotics, people often claim that we don’t have enough training data. But in my view, another big problem is that the data come from so many different domains, modalities, and robot hardware. Our work shows how you’d be able to train a robot with all of them put together,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a <a href="https://arxiv.org/pdf/2409.20537" target="_blank">paper on this technique</a>.Wang’s co-authors include fellow EECS graduate student Jialiang Zhao; Xinlei Chen, a research scientist at Meta; and senior author Kaiming He, an associate professor in EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented&nbsp;at the Conference on Neural Information Processing Systems.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1730247515325-MIT-HeteroGenRobot-02-press.jpg" style="width: 100%px;" class="fr-fic fr-dib">A figure shows how the new technique aligns data from varied domains, like simulation and real robots, and multiple modalities, including vision sensors and robotic arm position encoders, into a shared “language” that a generative AI model can process. Image: Courtesy of the researchers<h2>Inspired by LLMs</h2>A robotic “policy” takes in sensor observations, like camera images or proprioceptive measurements that track the speed and position a robotic arm, and then tells a robot how and where to move.Policies are typically trained using imitation learning, meaning a human demonstrates actions or teleoperates a robot to generate data, which are fed into an AI model that learns the policy. Because this method uses a small amount of task-specific data, robots often fail when their environment or task changes.To develop a better approach, Wang and his collaborators drew inspiration from large language models like GPT-4.These models are pretrained using an enormous amount of diverse language data and then fine-tuned by feeding them a small amount of task-specific data. Pretraining on so much data helps the models adapt to perform well on a variety of tasks.“In the language domain, the data are all just sentences. In robotics, given all the heterogeneity in the data, if you want to pretrain in a similar manner, we need a different architecture,” he says.Robotic data take many forms, from camera images to language instructions to depth maps. At the same time, each robot is mechanically unique, with a different number and orientation of arms, grippers, and sensors. Plus, the environments where data are collected vary widely.The MIT researchers developed a new architecture called Heterogeneous Pretrained Transformers (HPT) that unifies data from these varied modalities and domains.They put a machine-learning model known as a transformer into the middle of their architecture, which processes vision and proprioception inputs. A transformer is the same type of model that forms the backbone of large language models.The researchers align data from vision and proprioception into the same type of input, called a token, which the transformer can process. Each input is represented with the same fixed number of tokens.Then the transformer maps all inputs into one shared space, growing into a huge, pretrained model as it processes and learns from more data. The larger the transformer becomes, the better it will perform.A user only needs to feed HPT a small amount of data on their robot’s design, setup, and the task they want it to perform. Then HPT transfers the knowledge the transformer grained during pretraining to learn the new task.<h2>Enabling dexterous motions</h2>One of the biggest challenges of developing HPT was building the massive dataset to pretrain the transformer, which included 52 datasets with more than 200,000 robot trajectories in four categories, including human demo videos and simulation.The researchers also needed to develop an efficient way to turn raw proprioception signals from an array of sensors into data the transformer could handle.“Proprioception is key to enable a lot of dexterous motions. Because the number of tokens is in our architecture always the same, we place the same importance on proprioception and vision,” Wang explains.When they tested HPT, it improved robot performance by more than 20 percent on simulation and real-world tasks, compared with training from scratch each time. Even when the task was very different from the pretraining data, HPT still improved performance.“This paper provides a novel approach to training a single policy across multiple robot embodiments. This enables training across diverse datasets, enabling robot learning methods to significantly scale up the size of datasets that they can train on.&nbsp;It also allows the model to quickly adapt to new&nbsp;robot embodiments, which is important as new robot designs are continuously being produced,” says David Held, associate professor at the Carnegie Mellon University Robotics Institute, who was not involved with this work.In the future, the researchers want to study how data diversity could boost the performance of HPT. They also want to enhance HPT so it can process unlabeled data like GPT-4 and other large language models.“Our dream is to have a universal robot brain that you could download and use for your robot without any training at all. While we are just in the early stages, we are going to keep pushing hard and hope scaling leads to a breakthrough in robotic policies, like it did with large language models,” he says.

Researchers filmed multiple instances of a robotic arm feeding co-author Jialiang Zhao's adorable dog, Momo. The videos were included in datasets to train the robot. Image: Courtesy of the researchers

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

A faster, better way to train general-purpose robots

<section id="isPasted">When it comes to inspections in the maritime environment, the status quo has long been marked by challenges that impede efficiency and incur significant costs. Routine vessel inspections, vital for maintaining operational integrity, optimizing fuel efficiency, and ensuring safety underwater, have traditionally relied on divers or drydocking methods, both of which are inherently costly and time-consuming.The emergence of remotely operated vehicles (ROVs) promises a more accessible and cost-effective alternative, yet their usability has been hindered by the perennial problem of navigation beneath vessels' hulls.This article provides a comprehensive exploration of Dead Reckoning and the seminal work undertaken by Deep Trekker in mini ROV technology, with a particular emphasis on its potential to transform routine vessel inspections. The focus is on highlighting the implications for fuel efficiency, defect detection, and the cost-effective optimization of vessel maintenance.This innovative breakthrough was achieved by Deep Trekker’s pioneering work on Dead Reckoning technology. These latest achievements have the potential to advance underwater vessel inspections by providing a practical solution to the long-standing challenge of underwater navigation.<img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/3t6Sd0J8AmQJovMiFiUn2a/a62bd6232bd4c359e78a51496aabe8c7/Group_Glamor_Shot_crop1.jpg" alt="DESCRIPTION HERE" width="800" height="356" class="fr-fic fr-dii"></section><section><h2>Applications of Dead Reckoning with Underwater Robotics</h2>Beyond just hull inspections, Dead Reckoning finds widespread application in the field of underwater robotics, where autonomous vehicles and ROVs navigate complex underwater environments for various tasks, including oceanographic research, underwater surveys, pipeline maintenance, and search and recovery, to name a few.In oceanographic research, ROVs equipped with Dead Reckoning systems can gather data on ocean currents, temperature gradients, water quality, and marine life distribution. By continuously updating their positions through Dead Reckoning calculations, ROVs can execute predefined survey patterns, enabling scientists to study ocean dynamics and ecosystem health.Similarly, in underwater inspections and maintenance operations, ROVs rely on Dead Reckoning to navigate complex underwater structures, such as offshore oil rigs and submerged pipelines. By accurately estimating their positions relative to target objects, ROVs can perform tasks such as visual inspections, structural repairs, and equipment installations with minimal human intervention.Dead Reckoning also plays a vital role in search and recovery (SAR) missions, where time-critical decisions depend on accurate positional information. Deep Trekker ROVs equipped with Dead Reckoning systems can systematically cover large search areas, narrowing down the search radius based on calculated positions and sensor feedback.Overall, the integration of Dead Reckoning with underwater robotics facilitates more effective navigation in challenging underwater environments, enabling a wide range of scientific, commercial, industrial, and humanitarian applications.</section><section><h2>The Significance of Dead Reckoning in Maritime Inspections</h2>Dead Reckoning is a key component in maritime operations, especially in underwater inspections, as it provides a fundamental means of tracking the position and movement of inspection equipment in underwater environments, such as ROVs. This enables inspectors to navigate, track, and collect data accurately in challenging subaquatic settings.When combined with other navigation aids and sensors, Dead Reckoning contributes to the safety, efficiency, and effectiveness of maritime inspections, ensuring that they meet their intended objectives.Here are some key points highlighting the significance of Dead Reckoning in maritime operations:<h3>Navigating Underwater Without GPS or Electronic Aids</h3>Dead Reckoning serves as a valuable alternative for navigation in GPS-denied environments like underwater regions where GPS signals may be obstructed or jammed. In underwater inspection scenarios, GPS signals are unavailable or unreliable due to the water's attenuation of satellite signals. Dead Reckoning can provide positional data when these systems are unavailable, allowing for underwater navigation without the assistance of GPS or optics in underwater environments.By harnessing a combination of inertial sensors, gyroscopes, accelerometers, and sophisticated algorithms, Dead Reckoning enables ROV operators to maintain accurate spatial awareness and navigate with confidence, irrespective of GPS availability.Operators can rely on Dead Reckoning’s positional data to guide the ROV along predetermined routes and conduct thorough inspections, all without the need for GPS or optics assistance.<h3>Maximizing Inspection Efficiency with Position Tracking</h3>Dead Reckoning enables inspectors to continuously estimate the position of Deep Trekker ROVs as they move underwater during inspections. This information is critical for ensuring that the inspection covers the intended area. When you know where you are, you know where you have already inspected, and you know where you need to go next. Up until now, there has not been an effective method for position tracking for underwater ROVs near ships.By integrating various sensors and algorithms, Dead Reckoning provides real-time feedback on the ROV's location, allowing inspectors to maintain control. By employing Dead Reckoning technology, inspectors can monitor the ROV's position relative to predefined waypoints or planned trajectories, ensuring that the inspection remains on course and covers all targeted areas effectively.<img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/76y0rV73H4Knpiq1h0jdGP/654a1b0426b093d3ada261e9c8bb45d7/gif-__ROV_MOVING_ALONG_HULL_OF_SHIP_WITH_TRACK_LINE_FOLLOWING.gif" alt="DESCRIPTION HERE" width="800" height="356" class="fr-fic fr-dii"></section><section><blockquote>"Real-time position tracking enables inspectors to adapt to changing underwater conditions or unexpected obstacles, allowing for dynamic adjustments to the inspection strategy as needed."</blockquote>By maintaining accurate spatial awareness throughout the inspection process, inspectors can assess underwater assets, identify potential issues, and make informed decisions to optimize maintenance and repair activities.<h3>Enhancing Underwater Safety</h3>When working underwater, safety is a primary concern, both for the ROV operators and any accompanying divers - and knowing the location of the ROV and divers is essential for safety.Dead Reckoning helps inspectors ensure that ROVs or divers do not stray into hazardous areas, such as underwater structures, shipwrecks, or areas with strong currents. By utilizing Dead Reckoning for tracking the precise location of ROVs relative to underwater structures and currents, potential risks can be identified and avoided, minimizing the likelihood of accidents or damage to equipment.<h3>Optimizing Maintenance Strategies through Data Driven Insights</h3>Positional data obtained through Dead Reckoning improves the quality of inspection data, enabling inspectors to navigate ROVs to specific areas of interest. This targeted approach ensures that inspections focus on the most important areas, reducing the chance of missing essential details and improving data accuracy.<img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/5sM6RDNMt3mhw0FFcZ4pfK/55f0e7b543ed7c1e8d6b733f221c3aa0/gif-__LOOKING_UP_CLOSE_AT_SHIP_WITH_DEAD_RECKONING_TRACK_LINE_ON_SCREEN.gif" alt="DESCRIPTION HERE" width="800" height="356" class="fr-fic fr-dii">This includes data such as the location of underwater structures, defects, or anomalies, which must be precisely recorded for effective analysis. Higher quality data can lead to more informed decision-making, potentially reducing the need for follow-up inspections that consume additional fuel and resources.The data collected by ROVs utilizing Dead Reckoning technology provides valuable insights that can optimize vessel maintenance strategies. By accurately documenting hull conditions and identifying areas requiring attention, ROVs allow maintenance teams to prioritize repairs, schedule cleaning operations, and implement proactive maintenance measures.This data driven approach minimizes downtime, reduces operational costs, and extends the lifespan of vessels, ultimately raising the bar for operational efficiency and profitability.</section><section><h3>Fuel Efficiency and Resource Management</h3>Dead Reckoning assists in efficient resource management by helping inspectors monitor the progress of inspections. It helps ROVs identify the optimal times to clean the ship, to ensure that the hull remains clear of biofouling. Fuel efficiency in maritime operations can be dramatically improved by regular inspections and keeping the hull clear of marine growth - which can potentially reduce fuel efficiency by up to 40% by creating drag from the rough surface.Minimizing fuel consumption not only saves money but also reduces the environmental impact of maritime operations. Dead Reckoning, by ensuring efficient maintenance schedules, contributes to sustainability efforts by reducing emissions associated with fuel burning.<img data-fr-image-pasted="true" src="https://images.ctfassets.net/kzewhs8e6cvu/5wlYcwl6ZPnGvJmvXR1PbL/23d532b66f95008b4391b9f560b8b9d5/Wed-Aug-7-12-56-13-2019-hi-res-date-removed.jpg" alt="Hull inspection biofouling" class="fr-fic fr-dii"></section><section><h2>Integration of Dead Reckoning with Advanced Sensor Technologies</h2>Dead Reckoning technology is often integrated with advanced sensor technologies to further enhance the capabilities of ROVs during vessel inspections. Deep Trekker’s innovative Sensor Fusion algorithms is a great example of this.Sensors such as acoustic positioning systems, imaging sonars, and 3D mapping cameras complement Dead Reckoning by providing additional spatial awareness and environmental data.This integration enables ROVs to navigate complex underwater environments more effectively and capture comprehensive inspection data for thorough analysis and decision-making.</section><section><h2>Future Prospects and Innovations in ROV-based Vessel Inspections</h2>Dead Reckoning is a fundamental navigation technique that ROVs and underwater vehicles use to estimate their position and navigate in GPS-denied or challenging environments. While it provides continuous position estimates, it's important to combine with other sensor data and correction methods to maintain accuracy, especially in dynamic underwater conditions.In the maritime industry where efficiency, accuracy, and cost-effectiveness are critical to operations, Deep Trekker's Dead Reckoning technology stands as an example of this innovation's potential to reshape long standing industry practices and overcome the historical challenges of underwater navigation.<blockquote>"The ongoing advancements in ROV technology, coupled with the continued refinement of Dead Reckoning algorithms, point to an exciting future for vessel inspections."</blockquote>Innovations such as autonomous navigation, machine learning algorithms, and advanced sensor fusion techniques promise to further enhance the capabilities of ROVs for conducting comprehensive and efficient inspections.As these technologies continue to evolve, ROVs equipped with Dead Reckoning will play an increasingly prominent role in maintaining vessel integrity and ensuring maritime safety in the years to come.</section><section><h2>The Deep Trekker Advantage</h2>Deep Trekker ROVs, engineered with rugged industrial-grade materials, are designed for versatility and durability. They offer immediate deployment and a comprehensive variety of advanced features. These ROVs are highly portable and quick to set up, featuring powerful LED lighting, a live 4K video feed, and up to 360-degree field of view, imaging sonar, and flexible sampling tools.Our experienced team is at your service to offer expert guidance for a wide spectrum of applications, ranging from inspecting hull vessels to water tanks to conducting underwater assessments across various industries. We ensure tailor-made solutions that precisely match your specific requirements. When you're prepared to acquire your Deep Trekker ROV, don't hesitate to <a href="https://www.deeptrekker.com/contact-us" target="_blank">get in touch</a> with us for a <a href="https://www.deeptrekker.com/request-quote" target="_blank">personalized quote.</a></section>

Operator piloting ROV with Dead Reckoning tracking

Discover how Deep Trekker's ROV technology transforms underwater navigation, overcoming traditional challenges in underwater positioning. 

Modernizing Vessel Inspections: Dead Reckoning for Mini ROVs

As you might recall from <a href="https://cypherrobotics.com/2024/10/17/cypher-robotics-puts-captis-on-the-global-stage-at-gitex-global-2024-in-dubai/?et_fb=1&PageSpeed=off" target="_blank">this post</a>, Cypher Robotics and its <a href="https://indrorobotics.ca/cypher-robotics-announces-captis-a-new-solution-for-industrial-scanning/" target="_blank">Captis</a> cycle-counting/inventory management solution recently attended the huge <a href="https://www.gitex.com/" target="_blank">GITEX GLOBAL 2024</a> event in Dubai. It’s the world’s largest technology and AI exhibition, with some 200,000 attendees. It was a *huge* show.But a lot of the work – and the opportunities – for both Cypher Robotics and InDro Robotics (which incubated Cypher and has a technology agreement with the company) took place away from the show floor. Cypher Robotics CEO and InDro Vice President Peter King spent much of his time in high-level meetings with executives from five of the largest companies in the United Arab Emirates.“These were C-Suite level meetings, where we were able to learn more about what these companies do – and discuss how both InDro and Cypher can offer solutions that could benefit them,” says King.These aren’t companies where you can simply call and ask for a meeting with high-level executives. There needs to be a catalyst to facilitate such discussions.And there was: The Government of Canada; specifically, the Canadian Consulate in Dubai.Below: Cypher Robotics CEO Peter King on the floor at GITEX GLOBAL 2024. Much of his time was spent off the floor, meeting with executives from the largest companies in the UAE<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729798081995-GITEX-Peter.jpg-2.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Cypher Robotics Booth at GITEX"><h3 id="isPasted">Trade Mission</h3>Among the many responsibilities of the Federal Government is promoting trade between Canadian firms and international clients. Sometimes, there are large “Team Canada Trade Missions” which are led by a Minister and often covered by media. With these missions, there’s a specific push on the Indo-Pacific region. On other occasions, however, the government pulls together smaller groups with a very specific focus. Months before GITEX GLOBAL 2024 was to take the world stage, planning began for a mission in Dubai that would take place the same week.Government officials identified five Canadian firms in the Canadian high-tech sector it felt might be a fit for the UAE market. InDro Robotics was invited to participate – and was the only company among those five from the robotics sector.“They identified that our solutions could be highlighted in Dubai – not only for trade reasons, but also to help solve some really hard problems,” says Peter King. “We were obviously really pleased to be one of a small handful of technology companies to be on the federal government’s radar.”Canada’s Consul General in Dubai, H.E. Tracy Reynolds, was at the helm of this program and coordinated a series of meetings with “the UAE’s most influential business and technology leaders,” reads a Government of Canada document outlining the program.“Consul General Reynolds will lead a two-day outreach program that will allow selected Trade Commissioner Service (TCS) clients to pitch their products and solutions to Dubai’s conglomerates, which are considered to be major buyers of ICT products and solutions. The meetings will also allow the delegation to learn about the latest technologies being adopted by these organizations,” it adds.<h3> </h3><h3 id="isPasted">Why UAE?</h3>Though historically an oil-driven economy, the United Arab Emirates has diversified greatly in recent years. It has evolved, according to the CIA’s World FactBook, “into a trade-oriented logistics and supply chain leader (with) strong foreign direct investment orientation; building trade and investment ties through partnership agreements…” The UAE Gross Domestic Product is the fourth highest in the Middle East (after Turkey, Saudi Arabia and Israel), with an estimated USD 719.733 billion GDP in 2023.In Dubai, the skyline has been utterly transformed over the past few decades. It’s home to the world’s tallest building, the Burj Khalifa, and other ultra-modern architecture. Known also for its luxury shopping and high-end autos, Dubai has also embraced technology in recent years. In fact, the local police count Tesla Cybertrucks among their fleet.Dubai has never been as dependent on oil as the other six Emirates that comprise the UAE – and Dubai has led the way in the UAE in terms of economic diversification. According to Wikipedia, “Oil production, which once accounted for 50% of Dubai’s gross domestic product, contributes less than 1% today. In 2018, wholesale and retail trade represented 26% of the total GDP; transport and logistics, 12%; banking, insurance activities and capital markets, 10%; manufacturing, 9%; real estate, 7%; construction, 6%; tourism, 5%. The International Herald Tribune described it as ‘centrally-planned free-market capitalism’.”In other words, Dubai – and the wider UAE – are a significant and growing global marketplace.Below: The Dubai Skyline at night. Photo by Ivan Siarbolin – https://www.pexels.com/photo/city-skyline-during-night-time-3787839/, CC0, https://commons.wikimedia.org/w/index.php?curid=95959711<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729798138316-Dubai_Skylines_at_night_Pexels_3787839-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Dubai Skyline"> <h3 id="isPasted">High-Level Agenda</h3>With that context, it’s clear why the Canadian Consulate in Dubai sees opportunity. Being on the ground, Canadian&nbsp;Consul General&nbsp;Tracy Reynolds,&nbsp;Deputy Consul General Anthony Finch and Trade Commissioner&nbsp;Arun Basandai have an insider’s vantage point into the key economic players in Dubai and the UAE.&nbsp;And so, over the course of two days, they accompanied representatives of the five invited Canadian companies to five different high-level meetings.The first was with one of the largest real estate developers in the entire UAE. Remember the earlier reference to the Burj Khalifa? This company owns it. In addition to real estate, its diverse portfolio includes&nbsp;retail, hospitality, and&nbsp;leisure. The firm’s Executive Director and its Head of Information Technology attended the meeting.We don’t want to get into the details, but it was an excellent discussion – which included how solutions from both InDro Robotics and Cypher Robotics might be useful to that firm.&nbsp;From there, it was off to a massive global investment company that has been a major driver of Dubai’s spectacular growth. It’s involved with 10 sectors,&nbsp;including real estate, hospitality, leisure&nbsp;&amp; entertainment, media, ICT, design, education, retail, manufacturing, and&nbsp;logistics and science. It owns hotels, parks, resorts, a huge arena, multiple large retail outlets – and is also involved in multiple projects to accelerate Dubai into a fully Smart City. There were fruitful discussions there as well.Meeting three was with one of the largest retailers in Dubai and the entire UAE with an emphasis on the fashion and lifestyle industry. On the food and beverage side, it runs multiple name brand franchises throughout the UAE and is the distributor/retailer of major fashion brands. It’s a huge company with a massive rolling inventory across several sectors. As with the previous meetings, all five Canadian technology companies had a chance to discuss their offerings.&nbsp;<h3>And There Was More…</h3>Once again, it was C-suite meetings with the full support of senior Consulate staff. The group met with the CEO and Chief Strategy and Technology Officer (CSTO) of the leading shopping mall, retail and leisure company across the Middle East, Africa and Asia. It owns and operates 27 major shopping malls, multiple hotels, cinemas, etc. and has assets in excess of USD 18 billion and 44,000 employees.The final meeting was with “a multinational retail franchise operator of 70 brands in 20 countries.” Those brands include Starbucks, Chipotle and Cheesecake Factory. The company runs hotels and major retail outlets with names you’d recognize. The company’s Chief Strategy and Digital Officer attended this meeting, and was able to learn about solutions offered by all five Canadian technology companies.“These meetings were a tremendous opportunity to learn not only about what these leading UAE companies do, but also explore some of the challenges they face with operations at that scale,” says King. “There was significant interest in solutions from both InDro and Cypher – and I’m confident these were just the first of many conversations to come.”Below: The Cypher Robotics cycle-counting and RFID scanning Captis, which can operate autonomously for five hours and also capture precision digital twins. Below that is the InDro Robotics Sentinel, designed for remote asset inspection, security and surveillance and digital twins<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729798194505-Captis-1-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Captis Robot"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729798212185-Sentinel-in-substation-1.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Sentinel Inspection Robot"><h3 id="isPasted">InDro's Take</h3>We are pleased to have been selected to take part in this trade mission in the United Arab Emirates – and are exceedingly grateful to the senior staff at the Canadian Consul General in Dubai. These were exceptional meetings and, potentially, the beginning of new business relationships.“These meetings were a significant step along Cypher’s long-term roadmap, which includes markets beyond North America,” says King. “Every business relationship begins with a discussion, and these were very productive introductory meetings for all of the Canadian firms on this trip. I’d like to extend our deepest thanks for Consul General Tracy Reynolds, Trade Commissioner Arun Basandani and Deputy Consul General Anthony Finch.”InDro Founder and CEO Philip Reece is also pleased.“These meetings were a remarkable opportunity for not only InDro and Cypher, but for four other innovative Canadian tech companies,” he says. “The Government of Canada recognizes the global shift toward Industry 4.0 and the role Canadian technology companies can play in that. We are pleased that InDro had this opportunity and extend our thanks to all those involved.”If you’d like to learn more about InDro Robotics solutions, contact us <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank">here</a>. For Cypher Robotics and Captis, reach out <a href="https://cypherrobotics.com/contact-us/" target="_blank">here</a>.Cover image of Dubai at top of story via Wikimedia Commons by Tim Reckmann, CC BY-SA 3.0

Exploring strategic partnerships and showcasing cutting-edge robotic technologies, InDro Robotics and Cypher Robotics attend pivotal trade discussions to expand their global impact.

InDro Robotics and Cypher Robotics attend High-Level Trade Meetings in Dubai

Continuously promote the implementation of industry applications to the ground Unitree B2 beyond the limit Maximum speed of 6m/s, sustained load of 40kg and sustained walking endurance of 5h. The comprehensive performance is two to three times that of existing quadruped robots worldwide! Adaptable to all terrains, large load, long-lasting endurance, and super athletic performance! Evolve, evolve, and evolve again! More introduction: www.unitree.com/en/b2 <h2> </h2>

Continuously promote the implementation of industry applications to the ground Unitree B2 beyond the limit Maximum speed of 6m/s, sustained load of 40kg and sustained walking endurance of 5h. The comprehensive performance is two to three times that of existing quadruped robots worldwide! 

Unitree releases B2, beyond the limits of hyper-evolution

In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries. There are also full-sequence diffusion models like Sora, which convert words into dazzling, realistic visuals by successively “denoising” an entire video sequence.Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have proposed a simple change to the diffusion training scheme that makes this sequence denoising considerably more flexible.When applied to fields like computer vision and robotics, the next-token and full-sequence diffusion models have capability trade-offs. Next-token models can spit out sequences that vary in length. However, they make these generations while being unaware of desirable states in the far future — such as steering its sequence generation toward a certain goal 10 tokens away — and thus require additional mechanisms for long-horizon (long-term) planning. Diffusion models can perform such future-conditioned sampling, but lack the ability of next-token models to generate variable-length sequences.Researchers from CSAIL want to combine the strengths of both models, so they created a sequence model training technique called “Diffusion Forcing.” The name comes from “Teacher Forcing,” the conventional training scheme that breaks down full sequence generation into the smaller, easier steps of next-token generation (much like a good teacher simplifying a complex concept).<iframe width="640" height="360" src="https://www.youtube.com/embed/m4rhzR-h-JU?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Diffusion Forcing found common ground between diffusion models and teacher forcing: They both use training schemes that involve predicting masked (noisy) tokens from unmasked ones. In the case of diffusion models, they gradually add noise to data, which can be viewed as fractional masking. The MIT researchers’ Diffusion Forcing method trains neural networks to cleanse a collection of tokens, removing different amounts of noise within each one while simultaneously predicting the next few tokens. The result: a flexible, reliable sequence model that resulted in higher-quality artificial videos and more precise decision-making for robots and AI agents.By sorting through noisy data and reliably predicting the next steps in a task, Diffusion Forcing can aid a robot in ignoring visual distractions to complete manipulation tasks. It can also generate stable and consistent video sequences and even guide an AI agent through digital mazes. This method could potentially enable household and factory robots to generalize to new tasks and improve AI-generated entertainment.“Sequence models aim to condition on the known past and predict the unknown future, a type of binary masking. However, masking doesn’t need to be binary,” says lead author, MIT electrical engineering and computer science (EECS) PhD student, and CSAIL member Boyuan Chen. “With Diffusion Forcing, we add different levels of noise to each token, effectively serving as a type of fractional masking. At test time, our system can “unmask” a collection of tokens and diffuse a sequence in the near future at a lower noise level. It knows what to trust within its data to overcome out-of-distribution inputs.”In several experiments, Diffusion Forcing thrived at ignoring misleading data to execute tasks while anticipating future actions.When implemented into a robotic arm, for example, it helped swap two toy fruits across three circular mats, a minimal example of a family of long-horizon tasks that require memories. The researchers trained the robot by controlling it from a distance (or teleoperating it) in virtual reality. The robot is trained to mimic the user’s movements from its camera. Despite starting from random positions and seeing distractions like a shopping bag blocking the markers, it placed the objects into its target spots.To generate videos, they trained Diffusion Forcing on “Minecraft” game play and colorful digital environments created within Google’s <a href="https://github.com/google-deepmind/lab">DeepMind Lab Simulator</a>. When given a single frame of footage, the method produced more stable, higher-resolution videos than comparable baselines like a Sora-like full-sequence diffusion model and ChatGPT-like next-token models. These approaches created videos that appeared inconsistent, with the latter sometimes failing to generate working video past just 72 frames.Diffusion Forcing not only generates fancy videos, but can also serve as a motion planner that steers toward desired outcomes or rewards. Thanks to its flexibility, Diffusion Forcing can uniquely generate plans with varying horizon, perform tree search, and incorporate the intuition that the distant future is more uncertain than the near future. In the task of solving a 2D maze, Diffusion Forcing outperformed six baselines by generating faster plans leading to the goal location, indicating that it could be an effective planner for robots in the future.Across each demo, Diffusion Forcing acted as a full sequence model, a next-token prediction model, or both. According to Chen, this versatile approach could potentially serve as a powerful backbone for a “world model,” an AI system that can simulate the dynamics of the world by training on billions of internet videos. This would allow robots to perform novel tasks by imagining what they need to do based on their surroundings. For example, if you asked a robot to open a door without being trained on how to do it, the model could produce a video that’ll show the machine how to do it.The team is currently looking to scale up their method to larger datasets and the latest transformer models to improve performance. They intend to broaden their work to build a ChatGPT-like robot brain that helps robots perform tasks in new environments without human demonstration.“With Diffusion Forcing, we are taking a step to bringing video generation and robotics closer together,” says senior author Vincent Sitzmann, MIT assistant professor and member of CSAIL, where he leads the Scene Representation group. “In the end, we hope that we can use all the knowledge stored in videos on the internet to enable robots to help in everyday life. Many more exciting research challenges remain, like how robots can learn to imitate humans by watching them even when their own bodies are so different from our own!”<hr>Chen and Sitzmann wrote the paper alongside recent MIT visiting researcher Diego Martí Monsó, and CSAIL affiliates: Yilun Du, a EECS graduate student; Max Simchowitz, former postdoc and incoming Carnegie Mellon University assistant professor; and Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering at MIT, vice president of robotics research at the Toyota Research Institute, and CSAIL member. Their work was supported, in part, by the U.S. National Science Foundation, the Singapore Defence Science and Technology Agency, Intelligence Advanced Research Projects Activity via the U.S. Department of the Interior, and the Amazon Science Hub. They will present their research at NeurIPS in December.

In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries.

Combining next-token prediction and video diffusion in computer vision and robotics

Back in 2022 we officially took the wraps off Sentinel – a workhorse robot aimed at remotely inspecting assets like electrical substations.Before we even designed and built Sentinel, our engineering and sales team put a lot of thought into what would need to go into such a robot. We knew, first and foremost, it had be rugged and capable regardless of terrain. It had to be nearly impervious to inclement weather. It had to be capable of transmitting even dense data from hundreds or thousands of kilometres away from the client. Plus, it had to be equipped with state-of-the-art sensors. Oh yes, it also needed to perform self-diagnostics and send back an easy to read overall health report.And then our team at Area X.O got to work.Below: The Original Sentinel<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729532791402-DSCF3838-jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Original Sentinel Robot"> <h3 id="isPasted">Invent, Enhance, Deploy</h3>You may have heard that phrase before. It’s a quote from Founder and CEO Philip Reece that succinctly encapsulates what InDro does. It has since become our tagline, and if you see us at a convention you’ll see it on our branding.The reason we emphasize this now is because InDro remains committed to this ethos. Once we have a product out in the field, we don’t rest on our laurels. We gather learnings from our testing, deployments, and – most importantly of all – our clients to see if we can make an already good product better.Sentinel featured high-power onboard compute, a 30x optical PTZ camera (complete with wiper!), a thermal sensor and more. It also carried the ROS1 software library onboard which – among other things – allowed Sentinel to instantly recognise any new sensors that were added or replaced. It came equipped with a high-speed modem for remote teleoperations. And it had a pretty good version of our own InDro Autonomy stack.During a demonstration from Washington State we connected over a 5G network to <a href="https://areaxo.com/" target="_blank">Area X.O</a> in Ottawa. The two locations were more than 4100 kilometres apart. At the Bellevue end, we had an Xbox controller connected to a laptop. That laptop was logged into a third-party console for remote operations.At the other end, Sentinel was awaiting instructions. We showed several people the basics of how to operate (and it doesn’t get more intuitive than an Xbox controller) and let them take a spin. You might well think, given the distance involved, there would have been a perceptible lag. There wasn’t (I was there). The response was so instantaneous it was almost like playing a video game. Plus, realtime data – including a video feed – was coming in from Sentinel’s sensors.&nbsp;<h3>The EPRI Deployment</h3>Testing robots – particularly robots like Sentinel – in a real-world environment is crucial. And, since Sentinel was designed for the remote inspection of critical assets like electrical substations, we caught the attention of <a href="https://www.epri.com/" target="_blank">EPRI</a> – the Electric Power Research Institute. The non-profit organization is constantly doing research, collaborating with more than 450 private companies across 45 countries globally.EPRI has multiple research facilities, including one in Lenox, Massachusetts. This particular location features an electrical substation that can be energised, de-energised – and can even simulate rain for testing purposes. So we took Sentinel down there and put it through its paces. We ran it through every variation of testing they could throw at it, including nighttime missions (with the substation both energised and de-energised) as well as with and without rain in each of those scenarios. (Yes, we did daytime missions as well.)The purpose was to see how well Sentinel could perform in that very much real-world setting. It did very well – though we aso learned some lessons. We discovered that gravel could occasionally get caught between the gears and the treads. And, more importantly, we found that capturing and delivering all that data while doing remote ops put a crushing demand on the compute (a Jetson Xaiver NX).“We were maxing out the CPU,” recalls Head of R&amp;D Sales Luke Corbeth, who was on that mission. “We were at pretty much 99 per cent usage.”Over the course of subsequent deployments and testing, we began to explore ways to improve Sentinel.Below: Data flows through the third-party console we were using at the time to monitor missions<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729532864220-Screenshot-2022-08-25-at-12.18.17-PM.png.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Sentinel Dashboard View"> <h3 id="isPasted">Upgraded Interior, Exterior</h3>We’ve always had a consistent vision for Sentinel and its use-cases.“The concept has basically gone unchanged since our initial vision,” says Corbeth. “We envisioned certain industries with an emphasis on the value of preventative maintenance. That has remained the focus.”But as we encountered new environments and challenges, we worked on ways to improve the build. We are now taking advantage of the latest in AI processing, giving these robots the NVIDIA Jetson AGX Orin Developer Kit for compute. It’s capable of up to 275 trillion operations per second (TOPS) – and is a huge leap forward in compute and AI recognition. Plus, you can throw literally everything at it and it doesn’t break a sweat.“We’re now able to operate – with all data streaming – at about 30 to 40 per cent of the CPU’s capacity,” says Corbeth.An optional platform upgrade brings us to the Bunker Pro, which has better weather protection (including skirts that prevent gravel from getting stuck) and longer run time. We moved to the latest models of PTZ and thermal cameras to ensure exceptional resolution and the best imagery in low-light situations. We integrated more pinhole cameras for 360° situational awareness and added a second depth perception camera. We can also add a range finder and two hemispherical LiDAR units, pending client needs. (There’s also the option of safety LiDAR units if Sentinel is deployed around people.)And that’s just for starters. In addition, InDro’s engineering team can add:<ul><li>New connectivity module for greater bandwidth and lower latency</li><li>Improved GPS and IMU units</li><li>The latest InDro Autonomy stack</li></ul>All of these capabilities ensure Sentinel can operate with precision in unfamiliar and GPS-denied environments. It returns to base and recharges wirelessly without any mechanical connection (thus avoiding a potential point of failure). &nbsp;It can capture flawless digital twins of entire facilities.And InDro is in control of all facets of production.“With the introduction of <a href="https://indrorobotics.ca/from-napkin-sketch-to-prototype-reality-indro-forge-does-it-all/" target="_blank">InDro Forge</a>, we’ve been able to do all of the design and manufacturing in-house,” says Corbeth.Below: Sentinel combines brains with brawn<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729534325015-DSCF7770-scaled.jpg.webp" style="width: 100%px;" class="fr-fic fr-dib" alt="Redesigned Sentinel"> <h3 id="isPasted">Mission Planning</h3>Remember how we mentioned originally operating Sentinel via a third-party console? Well, despite getting pretty low latency we found some things were lacking. We wanted a more intuitive and powerful system for controlling Sentinel and setting up autonomous missions. We also wanted a console with bulletproof security.So we built one. <a href="https://indrorobotics.ca/new-indro-controller-a-simple-solution-to-complex-robotics-missions/" target="_blank">InDro Controller</a> both improves the user experience and adds more power and customization features for the client. We asked Luke to walk us through how easy it is for a client to plan a mission.“So the robot will always begin its mission starting in the doghouse where it’s actively wirelessly charging, ready to deploy at a moment’s notice with full battery. Deployments can be automated and autonomous, or manually operated,” he explains.Most clients prefer to have autonomous missions. And InDro is happy to help set up optimal mission planning, with specific repeatable actions at various waypoints. The exceptional AI and machine vision onboard means that Sentinel can not only recognise a gauge – it can understand the reading.“So we help create the most efficient and safest route for the robot to follow. And at each of its specific waypoints, it’s capturing high-resolution photos, video or thermal data. It will remember, for example, to not only stop but to adjust the camera and zoom in on a specific predetermined asset. You’ll get actionable data with every mission,” adds Corbeth.Watch both Sentinel and InDro Controller at work in the video below<iframe width="640" height="360" src="https://www.youtube.com/embed/ZR29Pl6Q-2E??si=kkHGxopNxSWxVlQr&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe><h3 id="isPasted">InDro's Take</h3>There are even more enhancements on the way for Sentinel that we didn’t explore in this post. If you’re curious, you can check out even more features <a href="https://indrorobotics.ca/sentinel-inspection-robot/" target="_blank">here</a>.“Sentinel is an exceptionally well-engineered product – where every design decision was made with the client in mind,” says InDro Robotics Founder and CEO Philip Reece. “It has already drawn attention from those seeking a cutting-edge solution to autonomous remote inspections. A US client just purchased several for precisely that use-case, and we’ll be completing those builds shortly.”Interested in a demo? Get in touch with <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank">Luke Corbeth</a> and he’ll set you up.

A Deep Dive into our Sentinel Inspection Robot

Industrial Inspection, Reimagined

In today's industrial landscape, programming a robot has become a significant challenge. The lack of skilled labor, combined with the increasing complexity of systems, makes implementing automated solutions more difficult. Furthermore, the wide variety of programming languages specific to each manufacturer creates technological silos, further complicating the integration of robots.These obstacles hinder the widespread adoption of robotics, as customizing and maintaining each system requires specialized skills and considerable time. This is where<a href="https://flr.io/products/fuzzy-rtos">&nbsp;FuzzyRTOS</a> comes in, a universal programming platform designed specifically for industrial robots. This unique solution simplifies and unifies robot programming while ensuring compatibility with manufacturers' safety layers, which is crucial for meeting strict industrial safety standards.With FuzzyRTOS, companies can now overcome these challenges and deploy robots with increased flexibility and security, without compromising on quality or safety. The latest version, FuzzyRTOS V5, introduces new functionalities designed to address the flexibility, high-performance, and modularity challenges that industrial companies face today.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729525725098-Fuzzyrobot.jpg" style="width: 832px;" class="fr-fic fr-dib">FuzzyRTOS V5: Credit: FuzzyLogic<h2 dir="ltr" id="isPasted">FuzzyRTOS: A Concentrate of Technology and Optimization</h2>What is FuzzyRTOS? Designed to offer centralized and autonomous control, FuzzyRTOS drives industrial robots of the past, present, and future. The technical advantages of this approach are numerous:<ul><li dir="ltr">Optimized Trajectory Management: By directly managing robot actions, FuzzyRTOS enables smoother movements and real-time task planning. This reduces downtime and enhances the precision of repetitive tasks.</li><li dir="ltr">Increased Flexibility: &nbsp;FuzzyRTOS is compatible with various programming languages (Python, JavaScript, C++, etc.), allowing easy configuration of new automation workflows. This adaptability to specific production line needs makes it a unique solution.</li><li dir="ltr">Autonomy and Independence: Robotic task management is no longer dependent on FuzzyStudio, Fuzzy Logic Robotics' legacy software, enabling companies to operate in a standalone mode, free from software constraints.</li></ul><h3 dir="ltr">What's New in Version 5: Toward an Independent FuzzyRTOS</h3>With Version 5, Fuzzy Logic Robotics pushes the boundaries of industrial automation by introducing two major features: standalone mode and an integrated sequencer.<ul><li dir="ltr">Standalone Mode: Toward Complete Autonomy &nbsp;</li></ul>Now, FuzzyRTOS can operate without FuzzyStudio. This paradigm shift makes the system fully independent in production, with the ability to manage its own projects autonomously. Project information is stored in .fsproj files, centralizing all versions and configurations of a robotic project. &nbsp;&nbsp;Each FuzzyRTOS installation can now host a list of projects. Users can activate a specific project that will reload automatically upon reboot, ensuring operational continuity. &nbsp;<ul><li dir="ltr">&nbsp;Maintenance Mode: By connecting FuzzyStudio to FuzzyRTOS, a technician can retrieve and modify existing projects on the system, offering unprecedented flexibility during maintenance operations. &nbsp;</li><li dir="ltr">Monitoring Mode: Once a project is installed, FuzzyStudio can remain connected to serve as a graphical interface. In the future, this functionality will be complemented by a web dashboard, making interaction with the system even more intuitive and accessible.</li></ul><h2 dir="ltr">NoCode Sequencer: A New Way to Design Tasks</h2>&nbsp; V5 also marks the arrival of an integrated sequencer, designed to simplify the creation and management of automation workflows. Through a NoCode interface, end-users can now program and modify action sequences without reliance on a PLC programmer. This sequencer transfers part of the control currently managed by the PLC directly into the FuzzyRTOS system, allowing for increased customization and maximum responsiveness. &nbsp;&nbsp;Adding the sequencer is a real asset for industries seeking to optimize their processes and make production more agile. An .fsproj file can now contain multiple recipes or sequences, enabling quick program changes based on production needs.<h3 dir="ltr">Compatibility with Repplix and Other Programming Languages</h3>Fuzzy Logic Robotics has always prioritized making its solutions flexible and interoperable. FuzzyRTOS V5 continues along this path by being compatible with Repplix as well as several programming languages. This openness allows companies to maintain their existing infrastructures while integrating FuzzyRTOS, without needing to alter their production environments. &nbsp;Thanks to this compatibility, FuzzyRTOS seamlessly integrates into various environments, facilitating adoption in established production lines. This versatility is essential in a sector where automation needs are as diverse as they are demanding.<h2 dir="ltr"><iframe width="640" height="360" src="https://www.youtube.com/embed/cccIm7Rh8uU?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe>Conclusion: A Pillar of Industry 4.0</h2>By placing autonomy, flexibility/modularity, and performance at the core of its solution, Fuzzy Logic Robotics redefines industrial automation standards with FuzzyRTOS V5. By enabling autonomous project and recipe management, this system provides companies with a sustainable, scalable solution that adapts to modern production chain needs. &nbsp;FuzzyRTOS is not merely a control tool for robotic cells; it is the key to smarter, more responsive, and, above all, more accessible automation. By investing in this solution, industries gain a major asset to tackle the challenges of tomorrow, fully embracing the Industry 4.0 dynamic.Read more at <a href="http://flr.io" id="isPasted">flr.io</a>.

The widespread adoption of robotics is hindered by the complexity of system integration, lack of skilled labor, and diverse programming languages, but FuzzyRTOS addresses these challenges by providing a universal platform that simplifies robot programming.

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