personalrobot.dev

#Personal Robot | Development

#Digital Twin of Person

#Non Verbal Cue

#Motion Control

#Learning

#Robotic Platform

#Machine Learning

#Computer Vision

#Gesture Control

#Stuntronics

#Superhuman manoeuvres

#Task Intelligence

#Transferlearning

#Untethered dynamic movement

#Onboard sensing

#Flips, twists and poses with repeatability and precision

#Extremely precise aerial motion control

#Differentiable simulation

#Reinforcement learning

#Dynamic robotic performance

#Gymnastic action

#Autonomous figure

#Flips with repeatability and precision

#Twists with repeatability and precision

#Poses with repeatability and precision

#personalrobot.ai

#personalrobot.app

#personalrobot.dev

#Problem Detecting

#Help Calling

#autonomousrobot.app

#autonomousrobot.dev

#nanotech skin

#Prompt engineering

#Speech Recognition

#Large language model (LLM)

#Improving control policies

#Upgrading actuation

#Minimizing joint complexity

#Three-fingered grippers

#Indoor deployments

#Deploying n fleets

#Functionally safe 3D vision

#Stepping toward greater levels of autonomy

#Making robots knowledgeable about different types of objects

#How to grasp objects

#Avoiding humanoid winter

#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

#Ultra sonic piezo motor

#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

#Vision Language model | Data preparation, alignment techniques and optimization methods necessary for embedding visual understanding capabilities within resource-constrained environments

#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

#Multipurpose commercial humanoid | Potential for useful and reliable and affordable humanoids | Difficult problem making highly technical piece of hardware and software compete effectively with humans in labor market | Robots are not hard to build; but they are hard to make useful and make money with | Whole perception pipeline running at the framerate of sensors nowadays | All the technology is here now | Starting with surrogate robot from someone else to get autonomy team going while building own robot in parallel | Giving out a significant chunk of the company to early joiners | Combined efforts of the research community enables commercialization | Building team is really important

#Humanoid robots and fashion future | Shanghai, humanoid robots transcend fashion hype, reimagining design, challenging beauty norms, and unlocking metaverse opportunities | Convergence of fashion and technology | Human-machine collaboration in fashion | Genuine, emerging trend | Creativity, production, and human-machine interaction | Robots are becoming experimental platforms | Integration of robots into runway | Aesthetic Reinvention: designing beyond the human form | Fostering Human-Robot Collaboration From Runway to Production and Retail | Challenging Beauty Norms | Paving Way for Future Trajectories: The Metaverse of Fashion

#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

#Ethernet Cameras | Ethernet Vision

#Unitree R1 humanoid | Agile mobility: 24-26-DOF for adaptation to complex scenarios; its 2-DOF head enhances environmental perception | Lightweight structure, easy maintenance: ≤121cm agile form, ultra-lightweight at about 25kg, ready out-of-the-box to empower | Integrated with Large Multimodal Model for voice and images: Fully open control interfaces for joints and sensors, with support for mainstream simulation platforms | Height Width and Thickness（Stand): 1210x357x190mm | Degree of Freedom(Total Joints): 24 | Single Leg Degrees of Freedom: 6 | Single Arm Degrees of Freedom: 5 | Waist Degrees of Freedom: 2 | Head Degrees of Freedom: None | Dexterous Hand: NOT | Joint output bearing: Crossed roller bearings, Double Hook Ball Bearings | Joint motor: Low inertia high-speed internal rotor PMSM(permanent magnet synchronous motor,better response speed and heat dissipation) | Maximum Torque of Arm Joint: 约 2kg | Calf + Thigh Length: 675 | Forearm + Upper Arm Length: 435 | Joint Movement Space: Waist Joint：Y±150° R±30°, Knee Joint：-10°~+148°, Hip Joint：Y:±157° P:-168° ~+146° R:-60° ~+100° | Electrical Routing: Hollow + Internal Routing | Joint Encoder: Dual + single encoder | Cooling System: Local air cooling | Power Supply: Lithium battery | Basic Computing Power: 8-core high-performance CPU | Microphone Array: 4-Mic Array | Speaker: YES | WiFi 6 | Bluetooth 5.2 | Humanoid Binocular Camera | NVIDIA Jetson Orin Optional (40-100 Tops) | Smart Battery (Quick Release) | Charger | Manual Controller | Battery Life: about 1h | Upgraded Intelligency: OTA | Warranty Period: 8 Months

#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