autonomousrobot.dev
#Autonomous Robot Development Meta
#Robot Operating System | ROS | Open-source platform | Software tools and libraries to facilitate development of robotic applications | Drivers | Algorithms
#ROS 2 | The second version of the Robot Operating System | Communication, compatibility with other operating systems | Authentication and encryption mechanisms | Works natively on Linux, Windows, and macOS | Fast RTPS based on DDS (Data Distribution Service) | Programming languages: C++, Python, Rust
#Robotics development platform | Autonomous mobile robots (AMRs) | Robot arms | Manipulators | Humanoids | Simulation | Robot learning frameworks | GPU accelerated libraries | AI models | Reference workflows
#ROS Control Framework
#Multi-query planners
#Single-query planners
#Near-optimal planners
#Trajectory Optimization
#Search Based Planning Library
#Stochastic Trajectory Optimization
#Covariant Hamiltonian Optimization
#Dynamic Motion Primitives
#Thunder and Lightning algorithms
#Planning Scenes
#Collision aware planning
#Virtual maps of the environment
#Collision checking
#Flexible Collision Library
#Point Cloud Library
#6 degrees of freedom
#7 degrees of freedom
#Industrial Humanoids | Robotic coworker | Industrial automation shifting from classic, specialized robots to more general purpose robots | Robots that are more adaptable, quick to learn, and retaskable | Robots working together and supporting people | Robotic teammates
#Kryon Systems
#Anybotics | Autonomous inspection robotics
#Inverse kinematics
#Humanoid robots move onto fast track | Nationwide initiative in China to accelerate humanoid robot adoption across manufacturing, logistics, retail, healthcare and other sectors | Ministry of Industry and Information Technology | Assets Supervision and Administration Commission of State Council | Initiative to accelerate humanoid robot adoption across manufacturing, logistics, retail, healthcare and other sectors | Creating more than 100 high-value application scenarios
#Inverse Jacobian method
#Gradient projection method
#Mantis Robotics
#Legged Robots
#Machine Learning Engine
#Heuristic method
#Robot configuration
#Overlapping (shared) joints
#Stack of tasks
#Particle swarm optimization
#Position controller
#Velocity controller
#Force controller
#Libraries for generating target grasp
#Neural network for converting camera data to grasp poses
#Pre-Grasp, Grasp, Post-Grasp evaluation with heuristic pruning
#Manipulation pipeline development
#3D Perception
#Bounding convex decompositions of meshes
#LIDAR
#Object detection
#Image segmentation
#Deep neural network
#Simultaneous Localization and Mapping
#Point cloud segmentation
#Point cloud alignment
#Probabilistic model for visual perception
#Visual-inertial odometry
#Intrinsic camera calibration
#Extrinsic camera calibration
#Visual servoing tracking of objects for manipulation
#ROS Navigation stack
#3D headsets
#Solidworks assembly files
#CAD files
#URDF specifications
#Actuator
#ROS Control
#Geometric fabrics
#1550nm LiDAR | Advantages: safety, range, and performance in various environmental conditions | Enhanced Eye Safety: absorbed more efficiently by cornea and lens of eye, preventing light from reaching sensitive retina | Longer Detection Range | Improved Performance in Adverse Weather Conditions such as as fog, rain, or dust | Reduced Interference from Sunlight and Other Light Sources | More expensive due to complexity and lower production volumes of their components
#SLAM | Simultaneous Localization and Mapping
#Vector database
#Multi-task robot agent
#Robot fleet
#Resistive RAM (ReRAM) technology | onsemi Treo platform to provide embedded non-volatile memory | ReRAM integration into Bipolar CMOS DMOS (BCD) process | Potential alternative to flash memory | Demand for faster, more efficient, and scalable memory solutions increasing | Lower power consumption | Less vulnerable to common hacking tactics | ReRAM can be integrated easily into chip designs without interfering with power analog components
#A-list celebrity home protector | Burglaries targeting high-end items | Burglary report on Lime Orchard Road | Burglar had smashed glass door of residence | Ransacked home and fled | Couple were not home at the time | Unknown whether any items were taken | Lime Orchard Road is within Hidden Valley gated community of Los Angeles in Beverly Hills | Penelope Cruz, Cameron Diaz, Jennifer Lawrence, Adele and Katy Perry have purchased homes there, in addition to Kidman and Urban | Kidman and Urban bought their home for $4.7 million in 2008 | 4,100-square-foot, five-bedroom home built in 1965 and sits on 1¼-acre lot | Property large windows have views of the canyons | Theirs is one of several celebrity properties burglarized in Los Angeles and across country recently | Connected to South American organized-theft rings
#Professional athlete home protector | South American crime rings | Targeting wealthy Southern California neighborhoods for sophisticated home burglaries | Behind burglaries at homes of professional athletes and celebrities | Theft groups conduct extensive research before plotting burglaries | Monitoring target whereabouts and weekly routines via social media | Tracking travel and schedules | Conducting physical surveillance at homes | Attacks staged while targets and their families are away | Robbers aware of where valuables are stored in homes prior to staging break-ins | Burglaries conducted in short amount of time | Bypass alarm systems | Use Wi-Fi jammers to block Wi-Fi connections | Disable devices | Cover security cameras | Obfuscate identities
#Path planning
#Motion planning
#Dexterous robot | Manipulate objects with precision, adaptability, and efficiency | Dexterity involves fine motor control, coordination, ability to handle a wide range of tasks, often in unstructured environments | Key aspects of robot dexterity include grip, manipulation, tactile sensitivity, agility, and coordination | Robot dexterity is crucial in: manufacturing, healthcare, logistics | Dexterity enables automation in tasks that traditionally require human-like precision
#Agentic AI | Artificial intelligence systems with a degree of autonomy, enabling them to make decisions, take actions, and learn from experiences to achieve specific goals, often with minimal human intervention | Agentic AI systems are designed to operate independently, unlike traditional AI models that rely on predefined instructions or prompts | Reinforcement learning (RL) | Deep neural network (DNN) | Multi-agent system (MAS) | Goal-setting algorithm | Adaptive learning algorithm | Agentic agents focus on autonomy and real-time decision-making in complex scenarios | Ability to determine intent and outcome of processes | Planning and adapting to changes | Ability to self-refine and update instructions without outside intervention | Full autonomy requires creativity and ability to anticipate changing needs before they occur proactively | Agentic AI benefits Industry 4.0 facilities monitoring machinery in real time, predicting failures, scheduling maintenance, reducing downtime, and optimizing asset availability, enabling continuous process optimization, minimizing waste, and enhancing operational efficiency
#Field Foundation Model (FFMs) | Physical world model using sensor data as an input | Field AI robots can understand how to move in world, rather than just where to move | Very heavy probabilistic modeling | World modeling becomes by-product of Field AI.robots operating in the world rather than prerequisite for that operation | Aim is to just deploy robot, with no training time needed | Autonomous robotic systems applucations | Field AI is software company making sensor payloads that integrate with their autonomy software | Autonomous humanoid Field AI can do | Focus on platforms that are more affordable | Integrating mobility with high-level planning, decision making, and mission execution | Potential to take advantage of relatively inexpensive robots is what is going to make the biggest difference toward Field AI commercial success
#Large Language Model (LLM) | Foundational LLM: ex Wikipedia in all its languages fed to LLM one word at a time | LLM is trained to predict the next word most likely to appear in that context | LLM intellugence is based on its ability to predict what comes next in a sentence | LLMs are amazing artifacts, containing a model of all of language, on a scale no human could conceive or visualize | LLMs do not apply any value to information, or truthfulness of sentences and paragraphs they have learned to produce | LLMs are powerful pattern-matching machines but lack human-like understanding, common sense, or ethical reasoning | LLMs produce merely a statistically probable sequence of words based on their training | LLMs are very good at summarizing | Inappropriate use of LLMs as search engines has produced lots of unhappy results | LLM output follows path of most likely words and assembles them into sentences | Pathological liars as a source for information | Incredibly good at turning pre-existing information into words | Give them facts and let them explain or impart them
#Retrieval Augmented Generation. (RAG LLM) | Designed for answering queries in a specific subject, for example, how to operate a particular appliance, tool, or type of machinery | LLM takes as much textual information about subject, user manuals and then pre-process it into small chunks containing few specific facts | When user asks question, software system identifies chunk of text which is most likely to contain answer | Question and answer are then fed to LLM, which generates human-language answer in response to query | Enforcing factualness on LLMs
#Large Behavior Model (LBM) | Controlling the entire robot actions | Joint research partnership between Boston Dynamics and Toyota Research Institute | Collaboration aims to create a general-purpose humanoid assistant | Whole-body movements: walking, crouching, and lifting to complete tasks that involve sorting and packing
#Large Behavior Model (LBM) | Controlling the entire robot actions | Joint research partnership between Boston Dynamics and Toyota Research Institute | Collaboration aims to create a general-purpose humanoid assistant | Whole-body movements: walking, crouching, and lifting to complete tasks that involve sorting and packing
#AI generalist robot | Developing end-to-end language-conditioned policies | Taking full advantage of capabilities of humanoid form factor, including taking steps, precisely positioning its feet, crouching, shifting its center of mass, and avoiding self-collisions | Building policies process: 1. Collect embodied behavior data using teleoperation on both real-robot hardware and in simulation, 2. Process, annotate, and curate data to easily incorporate it into machine learning pipeline, 3. Train neural-network policy using all of the data across all tasks | 4. Evaluate the policy using a test suite of tasks | Policy maps inputs consist of images, proprioception, language prompts to actions that control robot at 30Hz | Leveraging diffusion transformer together with flow matching loss to train model | Dexterous manipulation including part picking, regrasping | Subtasks triggered by passing a high-level language prompt to the policy | Reacting intelligently when things go wrong | With Large Behavior Model (LBM), training process is the same whether it is stacking rigid blocks or folding a t-shirt: if you can demonstrate it, robot can learn it | Speeding up the execution at inference time without requiring any training time changes
#Teleoperation | High-Quality Data Collection for Model Training | Control system allows to perform precise manipulation while maintaining balance and avoiding self-collisions | VR headset for operators to fully immerse themselves in the robot workspace and have access to the same information as the policy, with spatial awareness bolstered by a stereoscopic view rendered using head mounted cameras reprojected to the user viewpoint | Custom VR software provides teleoperator with a rich interface to command robot, providing them real-time feeds of robot state, control targets, sensor readings, tactile feedback, and system state via augmented reality, controller haptics, and heads-up display elements | One-to-one mapping between user and robot (i.e. moving your hand 1cm would cause robot to also move by 1cm) | To support mobile manipulation, tracking on feet added and teleoperation control extended to support stance mode, support polygon, and stepping intent to match that of operator
#Policy | Toyota Research Institute.Large Behavior Model | Diffusion Policy-like architecture | Boston Dynamic policy | Diffusion Transformer-based architecture | Flow-matching objective | Conditioned on proprioception, images | Accepting language prompt that specifies objective to robot | Image data comes in at 30 Hz | Network uses a history of observations to predict an action-chunk | Observation space consists of images from robot head-mounted cameras along with proprioception | Action space includes joint positions for left and right grippers, neck yaw, torso pose, left and right hand pose, and left and right foot poses | Shared hardware and software across two robots aids in training multi-embodiment policies that can function across both platforms, allowing to pool data from both embodiments | Quality assurance tooling allows to review, filter, and provide feedback on data collected
#Simulation | Allows to quickly iterate on teleoperation system and write unit and integration tests | Performing informative training and evaluations that would otherwise be slower, more expensive and difficult to perform repeatably on hardware | Simulation stack is faithful representation of hardware and on-robot software stack | Ability to share data pipeline, visualization tools, training code, VR software and interfaces across both simulation and hardware platforms | Benchmarking policy and architecture choices | Incorporating simulation as a significant co-training data source for multi-task and multi-embodiment policies deployed on hardware
#Vision-language model (VLM) | Training vision models when labeled data unavailable | Techniques enabling robots to determine appropriate actions in novel situations | LLMs used as visual reasoning coordinators | Using multiple task-specific models
#AI models deployed in embedded systems at edge | Brushless DC motors | Hall effect sensors | Optical encoders | Sensorless motor control | Field-oriented control | Artificial intelligence at edge | Three fundamental modalities: vision, sound, and motion | Using AI models to infer information about device environment | Linear algorithms | Software and hardware combination | Deploying multiple AI models in embedded devices requires edge processors designed to run AI | Embedded systems using AI can be considered open | Sensor fusion utilizes combined data from multiple sensors | AI-based vision systems are more adaptable to natural variations inherent in object inspection | Objects can be identified and inspected more quickly with greater flexibility | Strong multimodal AI, a single model will process multiple types of data | Control algorithms will use inputs generated by AI, inferred from multiple sources of data | AI inferencing in data flow | AI-enabled image sensors are perfect for gesture detection | Event detection based on sound is an active area of development | On device learning in real time
#AI models deployed in embedded systems at edge | Brushless DC motors | Hall effect sensors | Optical encoders | Sensorless motor control | Field-oriented control | Artificial intelligence at edge | Three fundamental modalities: vision, sound, and motion | Using AI models to infer information about device environment | Linear algorithms | Software and hardware combination | Deploying multiple AI models in embedded devices requires edge processors designed to run AI | Embedded systems using AI can be considered open | Sensor fusion utilizes combined data from multiple sensors | AI-based vision systems are more adaptable to natural variations inherent in object inspection | Objects can be identified and inspected more quickly with greater flexibility | Strong multimodal AI, a single model will process multiple types of data | Control algorithms will use inputs generated by AI, inferred from multiple sources of data | AI inferencing in data flow | AI-enabled image sensors are perfect for gesture detection | Event detection based on sound is an active area of development | On device learning in real time
#Immediate.Measures to Increase American Mineral Production
#Critical minerals in Artificial Intelligence | At the core of AI transformation lies a complex ecosystem of critical minerals, each playing a distinct role | Boron: used to alter electrical properties of silicon | Silicon: fundamental material used in most semiconductors and integrated circuits | Phosphorus: helps establish the alternating p-n junctions necessary for creating transistors and integrated circuits | Cobalt: used in metallisation processes of semiconductor manufacturing | Copper: primary conductor in integrated circuits | Gallium: used in compound semiconductors such as gallium arsenide (GaAs) and gallium nitride (GaN) | Germanium: used in high-speed integrated circuits and fibre-optic technologies | Arsenic: employed as a dopant in silicon-based semiconductors | Indium phosphide: widely used in optical communications | Palladium: used in production of multi-layer ceramic capacitors (MLCCs) | Silver: the most conductive metal used in specialised integrated circuits and circuit boards | Tungsten: serves as a key material in transistors and as a contact metal in chip interconnects | Gold: used in bonding wires, connectors, and contact pads in chip packaging | Europium: enables improved performance in lasers, LEDs, and high-frequency electronics essential to AI systems and optical networks | Yttrium: improves the efficiency and stability of materials like GaN and InP, supporting advanced applications in photonics, high-speed computing, and communications technologies
#Critical minerals for Optics, Imaging & Advanced Materials | Graphite: high-speed electronics, advanced sensors, and thermal management systems | Copper: short-distance data transmission in AI data centres | Germanium: a key material in thermal imaging, night-vision optics, and fibre-optic communication systems | Indium: optical communication systems | Praseodymium: specific types of lasers and optical materials | Neodymium:solid-state lasers | Holmium: specialised laser systems, particularly medical and scientific applications
#Critical minerals for Power Supply & Batteries | Lithium: portable electronics, wearables, electric vehicles | Graphite: stores lithium ions during charging process and releases them during discharge | Manganese: used in various lithium-ion battery chemistries | Cobalt: critical to the performance of premium mobile and computing devices | Nickel: crucial for electric vehicles, high-performance electronics, and energy-intensive AI systems
#Robot offline programming (OLP) | Robot programming outside of production system without stopping production | With offline programming, operator can view product in CAD model, enabling welding of internal, hidden areas with robot | Generating programs fast in virtual robot cells from anywhere in the world | Repeatable quality with accuracy and minimal waste | Software validated and optimized programs | Process knowledge database | Virtual models of the production | Mastering complex welding quality and efficiency | Delivering customized, modular machines faster and with higher quality | Customization makes automation and flexible robot programming critical for maintaining productivity | Offline programming expertise of Delfoi Robotics | Visual Components platform | OLP is utilized not only when introducing new products but also in refining existing programs | Visual Components used as a layout design tool | Modeling digital replica of the welding station with software and testing welding possibilities | Doing all programs before machine itself has arrived in factory | After robot installation calibrate, touch up, and upload them to robot controller | Commissioning, involves calibration of designed robot cell for accuracy, ensuring that programs function accurately for a faster production ramp up | Ponsse: harvesters, robotic welding station | Duun Industrier: Norwegian heavy machinery manufacturer, robotic welding station, database optimizing welding procedures in Welding Procedure Specification (WPS) library making it easy to replicate best practices across different products |.Sandvik Mining: manufactures heavy-duty underground loaders and trucks with complex, multi-pass welds, uses IGM and Yaskawa welding robots | Canatu: develops and manufactures advanced carbon nanotubes, along with related products | Pintos: manufacturing of steel reinforcements | Photocentric: manufacturer specializing in photopolymers | Meconet: high-quality metal components | Valmet: process technology, automation solutions and services for pulp, paper and energy industries | Ouman: building automation and energy efficiency for properties | Casemet: steel enclosure solutions | Koja: air handling and fan solutions for ships and buildings | Mulberry: luxury leather goods | MSK Plast: custom-made plastic parts | Delroi targets manufacturing companies across U.S. and Canada | Delfoi collaborates with Oracle, SAP, and Microsoft
#Silicon Photonics | Chip-scale implementation of opto-electronic systems on silicon substrates | Electro-optic transceivers in both the short distance datacom and high-performance coherent optical communications segments | Light detection and ranging, LiDAR | Optical coherence tomography | Material integration | Advanced assembly concepts | Advanced signal processing schemes | Emerging applications in biology | Emerging computation platforms | aiXscale Photonics spin off
#SB53 | Law requires large AI model developers to publish frameworks on their websites including how company responds to critical safety incidents, assesses, manages catastrophic risk | Companies must report critical safety incidents to CA state within 15 days, or within 24 hours if a risk believed to pose an imminent threat of death or injury | Addressing catastrophic risk posed by advanced AI models, called frontier models | Law addresses risks in the context of an operator losing control of an AI system | Transparency report must include intended uses of a model, restrictions or conditions of using a model, how a company assesses and addresses catastrophic risk, and whether those efforts were reviewed by an independent third party | Rishi Bommasani, Stanford University, consulted Gov. Gavin Newsom | Excluded impact of AI systems on environment | Only targets companies that make $500 million in annual revenue | Excluded information companies characterize as trade secrets (common way to prevent sharing information about AI models) | Office of Emergency Services will produce anonymized report about critical safety incidents
#Industrial AI | Robotics | Simulation | Edge Computing Ecosystems | Humanoid robots | Scaling robot automation beyond isolated workcells | AI factories | Digital twins | AI-driven design | Mobile robots | AI in semiconductor industry
#Claws | Agents that act independently | Agent capable of grasping tools and pull information rather than just processing text | Claws can run continuously in background | Designed to run directly on PC | Can run shell commands | Can read and write files to reach specific objective | Work flow oriented | Self-evolving autonomy | NemoClaw open source stack | NVIDIA Nemotron | NVIDIA OpenShell runtime | NVIDIA Agent Toolkit | NemoClaw simplifies and secures AI agent deployment | NVIDIA Agent Toolkit provides full deployment stack
#Dynamic memory | Double Data Rate (DDR) technology | Only maintains its data while the device is powered
#Low Power Double Data Rate (LPDDR) memory | Used in applications like cell phones
#Event based vision | prophesee.ai | Combining neuromorphic sensing and bio-inspired processing to create event-based vision systems that function like eye and brain | Each pixel only reports when it senses movement | Building visual-tactile datasets for development of better learning systems in robotics | Helping robots grip and identify objects | Gesture recognition and tracking | Counting and measuring at a rate of >1,000 objects/sec | Inspecting objects at >10m/s with 100x less data to be processed | High-speed recognition applications with blur-free asynchronous event output (i.e OCR) | Measuring vibration frequencies from Hz to Khz | Understanding fine motion in scene | Understanding the finest motion dynamics hiding in ultra fast and fleeting events | Event-based sensors to track objects with low compute power | Object counting and gauging – pharmaceutical pill counting – Mechanical part counting
#Synthetic AI | Synthetic AI | AI models trained heavily on artificially generated synthetic data as well as advanced human-mimicking agents | Using synthetic approaches allows companies to bypass data shortages and navigate strict privacy regulations | It can inadvertently lead to model degradation and a perception gap in real-world performance | Strict Privacy Compliance: Eliminates exposure of personally identifiable information (PII) | Allows safe data sharing under regulations like GDPR or HIPAA | Massive Scalability: generates billions of targeted data points quickly | Developers can easily scale training sets without waiting on slow human collection | Exceptional Edge-Case training: simulates rare, high-value scenarios, useful for training autonomous vehicles or uncovering financial fraud | Lower Operational Costs: avoids expensive, bureaucratic logistics of gathering and labeling physical data | Highly Lifelike Interactions: synthetic conversational agents adapt their tone and fluidly understand human intent which drastically reduces rigid, robotic feel of standard chatbots | Training AI models recursively on synthetic data without real-world baseline updates can cause severe model degradation over time | Perception Gap": models can easily overfit and ace structured, quantitative benchmarks, yet, frequently stumbling when handling messy, unpredictable real-world scenarios | Amplified Biases: If seed dataset used to generate synthetic data contains any historical or cultural biases, generation algorithm will often inherit and heavily amplify those flaws | Lack of Genuine Innovation: synthetic data replicates existing statistical patterns and cannot discover entirely novel ideas, behaviors, or qualitative knowledge | Complex Infrastructure Demands: building accurate generative pipelines requires specialized mathematical expertise and validation servers to verify data fidelity
#Embracing robot.mechanical form | Celebrating raw engineering, functional aesthetics, and distinct physical identity | Honest materials: carbon fiber, brushed titanium, polished chrome | Visible kinematics: showcasing linear actuators, planetary gears, complex wiring harnesses | Form follows function: structuring silhouette around lifting capacity, range of motion, and heat dissipation | Distinct UI: replacing human-like eyes with advanced sensor arrays, LiDAR pods, and optical lenses | Fashion & architecture: inspiring avant-garde techwear, exoskeleton aesthetics, and structural expressionism
#BUILD America 250 Act | Federal framework for autonomous commercial motor vehicles operating in interstate commerce | Reducing state-by-state regulatory uncertainty | Helping fleets plan for broader deployment | Safety certification | Inspections | Remote operations | Incident response | Data reporting Cab-mounted warning beacons | House Transportation and Infrastructure Committee approved H.R. 8870 | Bipartisan, five-year surface transportation reauthorization package covering roads, bridges, transit, rail, highway safety and motor carrier safety programs | U.S. Department of Transportation required within two years of enactment to establish and maintain a performance-based safety standard for ADS-equipped commercial motor vehicles operating in interstate commerce | Manufacturers to certify that vehicles meet federal safety standard before operating under framework | Kodiak: framework significantly accelerate Kodiak ability to deploy, scale and commercialize autonomous freight operations across United States | Aurora: bill strengthens interstate commerce and establishes safety standards for nation highways | Torc Robotics: framework provides regulatory certainty needed to scale autonomous freight operations across national freight network | PlusAI: federal structure would give developers, OEMs, fleets, insurers, law enforcement and regulators a common set of expectations | Safety standard needed to include information on hardware and software, operational design domain, engineering methodology, hazard analysis, verification and validation processes, simulations, test environments, crash response, hazard alerting and cybersecurity | Autonomous commercial motor vehicle should demonstrate have ability to follow traffic laws, detect and respond to hazards, manage system failures and operate within a clearly defined operational design domain | Waabi: industry is moving from pilots to broad commercial deployments | Secretary of Transportation to establish a transportation rulemaking committee | Allowing fleets to use cab-mounted warning beacons as a replacement for traditional reflective warning devices | Kodiak, Aurora, PlusAI, Waabi, Gatik and Torc: autonomous trucking is moving from pilots toward broader deployment
#Enterprise humanoid robot Atlas | Material handling applications
#Tenson driven | Kenshiro robot | Musculoskeletal humanoid Developed by the University of Tokyo | Robot moves by using internal motors to pull flexible cables (artificial tendons) that contract like biological muscles, rather than relying on heavy motors placed directly inside its joints | Tendon-driven system mimics human anatomy | Electric motors placed inside torso or upper limbs wind up high-strength cables, creating tension that pulls on aluminum bones to flex or extend a joint | Kenshiro is modeled after a 12-year-old boy | It uses 160 artificial tendon muscles controlled by 93 brushless DC motors to copy the organic layout of human muscles, torso, and spine | Reduced Limb Weight: Keeping motors away from joints makes limbs incredibly light. This reduces inertia, meaning robot requires less power to swing its legs or arms quickly | Kenshiro tendon system integrates tension-controlled muscles and non-linear springs, allowing it to give way slightly under pressure and absorb shock just like human muscles | High Degrees of Freedom: Because cables take up far less space than bulky gearboxes, Kenshiro can pack 64 degrees of freedom (individual directions of movement), including a flexible S-curve spine and a highly complex neck
#Intelligent-robot development | Intelligent-robot model research | Robot-body research | Development of new intelligent-robot products | Intelligent-robot manufacturing base
#Think tokens in AI | Inside think tokens is AI Chain of Thought (CoT), which represents its internal reasoning process before it outputs a final answer | Reasoning contains: | Problem analysis: breaking down complex prompts into smaller, manageable parts | Fact retrieval: searching internal knowledge or planning search queries | Step-by-step logic: solving math, coding, or logic problems sequentially | Self-correction: catching mistakes, evaluating alternative approaches, and refining strategy | Safety checks: reviewing request against safety guidelines | Higher accuracy: giving AI time to think drastically improves its performance on complex tasks | Transparency: allows users to see exactly how AI arrived at a specific conclusion | Debugging: developers can look inside thoughts to find where a logic chain broke down | In AI interface like DeepSeek-R1 or OpenAI reasoning model, text between these tokens is hidden behind a collapsible Thinking Process dropdown so it does not clutter final response
#Chain of Though token | Chain of Thought token (CoT) | Any individual unit of data (word, syllable, or character) generated by Large Language Model (LLM) while it formulates its intermediate reasoning steps | Acts as model internal scratchpad | Allows midel to map out complex logic, solve multi-step problems, and self-correct before presenting a final conclusion | Autoregressive context: LLMs generate text one token at a time | Each CoT token produced serves as immediate context for the next token, building a step-by-step logic chain | CoT tokens function similarly to variables in a computer program, temporarily storing values and intermediate states required to solve broader task | Modern reasoning models allocate a specific internal thinking budget of tokens to handle complex problems, a higher number of thinking tokens usually correlates to better accuracy on difficult tasks | Visible CoT tokens are generated directly in visible text output, usually prompted by phrases like lets think step by step | Standard models use standard CoT prompting via Prompt Engineering Guide | Hidden (Internal) CoT Tokens are processed behind scenes in a native thinking phase before any text is shown to user | Advanced reasoning models separate compute stage from final response | If model must generate hundreds or thousands of intermediate tokens, time-to-response increases significantly | API providers charge for CoT tokens at standard output token rate, meaning thinking increases overall cost of query | Overthinking: models can waste tokens over-analyzing simple questions that they could have easily answered directly | Alternative frameworks like Chain of Draft (CoD) or compression tools like TokenSkip are used to dramatically minimize token footprint while keeping reasoning sharp
#Chain of Though token | Chain of Thought token (CoT) | Any individual unit of data (word, syllable, or character) generated by Large Language Model (LLM) while it formulates its intermediate reasoning steps | Acts as model internal scratchpad | Allows midel to map out complex logic, solve multi-step problems, and self-correct before presenting a final conclusion | Autoregressive context: LLMs generate text one token at a time | Each CoT token produced serves as immediate context for the next token, building a step-by-step logic chain | CoT tokens function similarly to variables in a computer program, temporarily storing values and intermediate states required to solve broader task | Modern reasoning models allocate a specific internal thinking budget of tokens to handle complex problems, a higher number of thinking tokens usually correlates to better accuracy on difficult tasks | Visible CoT tokens are generated directly in visible text output, usually prompted by phrases like lets think step by step | Standard models use standard CoT prompting via Prompt Engineering Guide | Hidden (Internal) CoT Tokens are processed behind scenes in a native thinking phase before any text is shown to user | Advanced reasoning models separate compute stage from final response | If model must generate hundreds or thousands of intermediate tokens, time-to-response increases significantly | API providers charge for CoT tokens at standard output token rate, meaning thinking increases overall cost of query | Overthinking: models can waste tokens over-analyzing simple questions that they could have easily answered directly | Alternative frameworks like Chain of Draft (CoD) or compression tools like TokenSkip are used to dramatically minimize token footprint while keeping reasoning sharp
#NVIDIA.$500 billion initiative | Establishes independent compute financing platforms to turn AI hardware into a brand-new financial asset class | Announced via Memorandums of Understanding (MOUs) in August 2026 | NVIDIA has partnered with six of Wall Street premier asset managers | Apollo Global Management | BlackRock | Blackstone | Brookfield Asset Management | Goldman Sachs | KKR | Core objective is to treat graphics processing units (GPUs) and AI factories as income-generating infrastructure, similar to commercial real estate, toll roads, or aircraft leases | Third-Party Capital Mobilization | Wall Street firms will source, vet, and individually underwrite loan proposals for hyperscalers, frontier labs (like OpenAI and Anthropic), and enterprises | GPUs as Loan Collateral: borrowers secure massive loans using NVIDIA hardware itself as collateral, functioning on premise that compute has clear intrinsic resale and rental value | NVIDIA Financial Backstop: NVIDIA provides residual support, promising to backstop up to 25% (or $125 billion) of individual deals to stabilize hardware value if a borrower defaults | Secondary Liquidity Ecosystem: If a client defaults, NVIDIA and its partners plan to quickly re-rent or relocate affected chips to other waitlisted data centers, protecting enders from total capital loss | Wall Street financial engineering introduces massive benefits for NVIDIA corporate ecosystem and financial metrics | Handing credit analysis and capital pool over to independent institutional giants validates genuine market demand | Unlocks kong-duration revenue share: beyond selling silicon upfront, NVIDIA could capture up to a 35% revenue share above breakeven from these platforms, potentially adding a 10%+ upside to FY2029 earnings per share | Secures CUDA ecosystem: by subsidizing and simplifying financing hurdle for startups and enterprises, NVIDIA locks customers deeper into its proprietary CUDA software stack, keeping competitors out | BlackRoc CEO Larry Fink likened this initiative to 1970s creation of mortgage-backed securities, calling it the next era of financial engineering | If underlying economic demand for AI tokens and services keeps pace, this structure ensures NVIDIA remains undisputed gatekeeper of global infrastructure | Bringing independent long-term institutional capital to infrastructure market demand is genuine | 35% revenue share above breakeven could provide more than 10% upside Nvidia fiscal 2029 earning
#GMSL2 (Gigabit Multimedia Serial Link 2) | High-speed, automotive-grade digital interface used in robotics to transmit uncompressed high-resolution video, control data, and power over a single cable with near-zero latency | Developed by Maxim Integrated (now Analog Devices) | Acts as a highly reliable neural highway connecting cameras and sensors to a robot central processing brain (such as NVIDIA Jetson or industrial PC) | GMSL2 relies on hardware technique called SerDes (Serializer / Deserializer) | At camera a tiny Serializer chip takes massive, parallel raw video data from camera sensor and squashes it into a single, high-speed serial stream | Through cable stream travels down a single coaxial or Shielded Twisted Pair (STP) cable | At host computer a deserializer chip on carrier board converts serial data back into parallel format (usually MIPI CSI-2), handing it off to AI processor instantly | Key benefits for robotic systems include ultra-low latency: unlike Ethernet or Wi-Fi, GMSL2 does not compress video which guarantees near-instantaneous transmission, allowing Autonomous Mobile Robot (AMR) traveling at high speeds to detect obstacles and brake in real time | Long reach & thin cabling: GMSL2 can transmit 4K data flawlessly over single cables up to 15 meters (50 feet) | Power Over Coax (PoC): a single wire carries uncompressed video, bidirectional control commands (like I2C/UART to adjust exposure), and physical power needed to run camera, which massively slashes robot weight, clutter, and cable management failure points | Immunity to heavy industrial noise: Warehouses and manufacturing floors are flooded with electromagnetic interference (EMI) from heavy motors and power lines, GMSL2 chips use High Immunity Mode (HIM) and programmable spread spectrum clocking to guarantee zero dropped frames in chaotic electronic environments | Perfect multi-camera sync: for robots utilizing 360° surround-view setups or stereoscopic depth-sensing, a single GMSL2 deserializer can aggregate and lock multiple camera feeds in perfect timestamp synchronization | Common robotics use cases:Autonomous Mobile Robots (AMRs) | Industrial Robotic Arms | Agricultural & All-Terrain Robots
#Unitree IPO in Shanghai | Unitree Robotics became the first humanoid robot maker listed on A-share market in Shanghai | The first humanoid company to go public in mainland China | Chinese robotics giant Unitree soars in stock market debut | Unitree Robotics stock soars 460% in Shanghai IPO debut | Shares of Unitree surged nearly 630% in China, before closing up 460% | Company raised $900 million in its debut | Strategic investors include Chinese AI startup DeepSeek, a group associated with tech giant Tencent, and several state-owned utility companies | Retail traders were 5,000x oversubscribed | China humanoid market is predicted to grow from $2 billion 2026 to $15 billion by 2030 | IPO price of 150.80 yuan with stock closing at 845 yuan represented a 460 per cent gain | Unitree move toward capital market sends important signal: humanoid robotics and embodied AI are moving beyond technology development, competition-based validation and product iteration toward industrialization, scalability and broader recognition from capital market | Hangzhou-based company offered ca. 40.45 million shares at 150.8 yuan each, representing a price-to-earnings ratio of 219.23 | Its cumulative quadruped robot shipments exceeded 33,000 units, with a global market share of nearly 60 percent | Unitree specializes in quadruped and humanoid robots | Unitree has fully self-developed core components, including motors, reducers, controllers, and LiDAR | Company posted revenue of about 1.15 billion yuan in the first half of 2026, up 48.54 percent year on year | Funds raised will be put toward intelligent robot model development, robot hardware R&D, new product development and manufacturing base construction | Business moves from robot manufacturing toward building a broader ecosystem for high-performance general-purpose robots | Unitree founder Wang Xingxing was quoted by Shanghai Securities News | Unitree unveiled its new humanoid robot Superman | Global humanoid robot shipments are projected to exceed 510,000 units by 2030
#Yocto Project | Officially supported by NVIDIA | Starting with release of JetPack 7.2 (Jetson Linux R39.2) | Marked a monumental shift from a purely volunteer, community-driven effort to a first-party, production-validated engineering path for NVIDIA Jetson and Thor hardware | By partnering directly with OpenEmbedded for Tegra (OE4T) community, NVIDIA co-maintains critical Board Support Package (BSP) layer known as meta-tegra | This combination allows commercial engineering teams to combine high-performance AI libraries of NVIDIA with deterministic, immutable, and hardened infrastructure of Yocto | Key Technical Pillars | Custom Edge AI App |NVIDIA AI Compute Stack (CUDA, TensorRT) |meta-tegra BSP Layer (NVIDIA-validated Yocto Recipes) |Yocto Project / Poky Base (Deterministic Immutable OS) |Hardware Target (Jetson Orin Nano / AGX / Thor) Core Layer (meta-tegra), OE4T meta-tegra on GitHub | OE4T maps NVIDIA proprietary hardware binaries, downstream kernels, and boot firmware into BitBake recipes | It handles everything from low-level flashing scripts to injection of Linux for Tegra (L4T) user-space libraries | JetPack 7.2 Paradigm Shift: developers used Ubuntu-based JetPack roots, which are mutable, prone to package drift, and too bloated for deeply embedded systems | NVIDIA Integration: Official validation of recipes for CUDA, TensorRT, and nvidia-docker directly in Yocto pipeline | Pre-Built Images: NVIDIA hosts pre-built Yocto reference binaries (such as demo-image-full) on official NVIDIA JetPack Downloads Page for immediate evaluation | Modernized Toolchain: support is closely aligned with modern releases like Yocto 6.0 (Wrynose LTS) and Yocto 6.1 (Blacksail)
#IMU | Inertial Measurement Unit | Modern AGVs and factory robots rely on precise motion and attitude feedback as one part of their navigation and control systems to operate safely and efficiently in environments where external positioning signals are limited or unavailable | Indoor machines must navigate around racks, equipment and moving obstacles while maintaining stable orientation during acceleration, turning, lifting and manipulation tasks | IMU provides continuous motion reference needed by vehicle controller to understand short-term movement changes and maintain stable control throughout production cycle | 6-degree-of-freedom MEMS inertial measurement unit outputs tri-axis angular rate, tri-axis acceleration and temperature | Embedded VRU algorithm turns that data into roll and pitch of carrier | IMU is what lets AGV track how far it has turned between two waypoints, stacker detect that its mast is tilting, and inspection robot know orientation of its own body before vision system interprets what it sees | One time reference for LiDAR, Vision and GNSS | Redundancy for industrial operation with IMU carrying multiple gyroscopes and accelerometers