autonomousdevice.dev

#Autonomous Device Development Meta

#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

#Machine Learning

#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

#Robot Operating System

#Embedded System SDK

#Real Time OS Development Kit

#Edge Device SDK

#Robotics development platform | Autonomous mobile robots (AMRs) | Robot arms | Manipulators | Humanoids | Simulation | Robot learning frameworks | GPU accelerated libraries | AI models | Reference workflows

#Collaborating

#Autonomous Things

#AuT

#AI Algorithms

#Smart Home Device

#Autonomous Software

#Machine With Sensors

#Analytical Capabilities

#Data Based Decisions

#Autonomously Completing Tasks

#Multi ProtocolbApproach

#Asset Location Connection

#Asset IoT

#Micro Controlled Platform

#IoT Cloud Connection

#Real Time Monitoring

#Statistics

#Environmental Data Collection

#PerformancevAnalysis

#Motion Sensor

#Vibration Sensor

#WiFi Connection

#Satellite Connection

#SLAM | Simultaneous Localization and Mapping

#Ultra sonic piezo motor

#Agentic workflow

#Vector database

#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

#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

#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

#Incremental encoder | Rotary or linear sensor | Generates a series of pulses as it moves | Using two output channels to determine direction | Measures relative motion by counting pulses from a known starting position | Providing high resolution and speed measurement capabilities | Unlike absolute encoder, incremental encoder requires a reference point or homing procedure to establish an initial position | It does not maintain absolute position information when powered off

#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

#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

#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

#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

#Agent Development Lifecycle (ADLC) | Build | Test | Deploy | Monitor | Improve over time

#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

#Embedded Vision Summit | Incorporating computer vision and AI in products | Vision-language, large multimodal, large language and vision-language-action models | Small language models, compact VLMs, model optimization and compression, quantization, pruning, distillation and efficient edge inference | World models and physical AI systems that perceive, reason, plan and act in real-world environments, including generative and joint-embedding predictive architecture (JEPA) approaches | Deep neural networks, transformer-based networks, state-space models and neuromorphic algorithms such as spiking neural networks | Learning at edge: few-shot learning, continuous learning | 3D perception, including SLAM and scene understanding using depth sensors, radar, lidar | Sensor integration and fusion in ML-based systems—vision, depth, audio, haptic

#LPDDR5 | Low Power Double Data Rate 5 | High-speed, highly efficient RAM standard designed for mobile devices, laptops, and AI edge processors | Delivers data rates up to 6400 Mbps while reducing power consumption by roughly 20-30% compared to previous generations (like LPDDR4X)

#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

#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)