personalrobot.ai

#Personal Robot AI Meta

#Laser-based heart monitoring robot | Laser device detecting heartbeat, breathing rate, and muscle activity from up to meter away, without requiring any wires or contact

#Object localization

#Object tracking

#Visual servoing

#Spatially aware manipulation

#Hand-eye coordination

#Semantic scene understanding

#AI and machine learning

#3-D reconstruction

#Unstructured Robotics | Performing tasks not predetermined or predefined | Real-time perception

#Robotics Perception

#Human-assisted robotics

#Haptic technology

#Object recognition

#Machine learning

#Redundant degrees of freedom

#Avatar robot

#Remote avatar robot

#Physical movement

#Virtual movement

#Emotional movement

#GPT language

#Sensors tracking movement

#Face recognition

#Voice recognition

#Detecting emotions

#Detecting age

#Communicating common expressions like astonishment and surprise

#Communicating gestures like yawning and shrugging

#Artificial neural network

#Having ear for music

#Non Verbal Cue

#Machine Learning

#Gesture Control

#Surrounding Sensing

#Owner Learning

#Task Intelligence

#Health Monitoring

#Problem Detecting

#Death Management

#Humanoid robot

#Believable robotic character

#Dynamic robotic performance

#Actuated figure

#Untethered, high-velocity gymnastic action robustly and repeatably

#Stuntronics

#Socially intelligent machine

#Cognitive intelligence

#AI Interactions

#Human-like robot

#Inference Engine

#Conversational AI assistant

#Interactive co-pilot

#Token generation

#Models of muscle tissue

#Musculoskeletal health

#Cardiovascular health

#Action recognition model

#Neurological degeneration

#Neuromuscular junctions

#Synthetic data generation (SDG)

#Training

#Stimulation

#Teleradiology

#Autonomous mobility assistance

#Blind individuals

#Visually-impaired individuals

#Regain independence

#Geometric fabrics

#Trained policies

#Reforcement Learning (RL) | Does not rely on labeled data | Learning through trial and error using reward-punishment | Transferring simulation results to real robotic hardware remains challenge | Need to train policies that generate a variety of agile behavior on physical hardware to achieve novel, robust, and practical locomotion behavior | Robot capability to manipulate objects and fixtures, such as doors and levers, in conjunction with locomotion would significantly enhance its utility | Exploring full-body contact strategies | Exploring high-performance, whole-body locomotion and tasks requiring full-body contact strategies, such as dynamic running and full-body manipulation of heavy objects | Developing close coordination between arms and legs | Developing reinforcement learning to generate behavior during complex contact events without imposing strict requirements | Reinforcement learning algorithms for humanoid locomotion | Reinforcement learning algorithms for humanoid manipulation policies | Prototyping reinforcement learning algorithms | Pybullet | IssacSim | Mujoco | Training RL policies in simulation

#Context length

#Tokens/s speed

#AI infrastructure

#AI inference technology

#Digital Twin of Person

#Retrieval-Augmented Generation (RAG) | Enhancing large language models (LLMs) by integrating external knowledge sources | Improving accuracy and relevance of generated responses | Allows LLMs to retrieve pertinent information from databases or documents before generating answers | Reducing errors and hallucinations typical in traditional models

#SLAM | Simultaneous Localization and Mapping

#Body analysis

#Facial expression analysis

#Eye gaze control

#Face segmentation

#CRUD | Create, Read, Update, and Delete | Four fundamental operations for managing persistent data in applications

#Ubiquitous

#AIoT space

#Electric actuator | Device converting electrical energy into mechanical motion | Using electric motor to create movement or apply force

#Legged robot

#Multimodal access

#Electromyography (EMG)

#Spatial control

#Dexterity

#Bionics

#Exoskeleton

#Cogging torque

#Cobot

#Soft robot

#Pneumatic device

#Soft silicone

#Pneumatic channel

#Agentic workflow

#Learning Management System (LMS)

#Time To First Token (TTFT)

#Build simulation stack

#Close sim-to-real gap

#Building contact-rich physics simulation

#Scaling up reinforcement learning algorithms

#Drive AI model evaluation and iteration speed

#Build ML robotics simulation

#Work across AI stack

#Build data engine to log, clean, label data for foundation model training

#Pybullet

#IssacSim

#Mujoco

#Blender

#Maya

#GPU simulation

#NoSQL

#Python | Testing large codebases in Python

#Deep Learning Frameworks | Pytorc | TF | JAX

#Managing ML compute clusters

#Linear algebra

#Supervised machine learning

#Robot Opersting System | ROS | ROS2

#Open source LLM technology

#Simulating and validating robots, for multiple domains

#Extracting key sction points

#Skeletal data

#Scene

#Character assets

#Asset path

#Programming new action for subject

#Character primitive

#Re-rendering stage

#Spatial-temporal graph convolutional network (ST-GCN)

#3D skeleton data

#Action recognition

#Zero-shot inferencing

#Artificial General Intelligence (AGI) | AI capable of understanding, learning, and performing any intellectual task that a human can

#Core knowledge systems | Either innate or developed early on in humans and some non-human animals

#Objectness | Knowledge that world can be parsed into objects that have certain physical properties

#Numerosity | Knowledge of small quantities | Notions of smallest, argest, greater than, less than

#Basic geometry and topology | Knowledge of lines, simple shapes, symmetries, containment, and copying

#Agents and goals | Knowledge some entities are agents who have their own intentions and act to achieve goals

#Few-shot learning | Each task has only a few examples from which an abstract rule must be inferred

#Text-only language model

#Multimodal foundation model | Able to deal with both text and images

#Polymathic AI | Developing machine learning models integratung knowledge across various scientific disciplines | Training AI with diverse datasets | Providing open-source training datasets | Fostering collaboration among researchers | Creating foundation models to identify commonalities across disciplines

#Robot Teaching Method using Hand Gestures and Poses

#Programming robots by human demonstration

#Convolution Neural Network (CNN) to recognize gestures

#Robot motion primitives

#Managing primitives in robot system

#Behaviour-based programming platform

#Extensible Agent Behavior Specification Language (XABSL)

#Hand motion sequence

#Robot pick-and-place task

#Human-Robot interaction (HRI)

#Understanding human intention through human behaviours

#Hand gesture recognition system

#Training database

#Gesture modelling

#Task goal

#Behaviour-based robot system

#Gesture command

#Robot task sequence

#CNN-based gesture recognition system

#Multi-tasking scheduling to process images from cameras and robot encoders, and command robot to behave under proper conditions

#7 Degrees of Freedom (7-DOF) | Offers kinematic redundancy | Allows multiple joint configurations to achieve the same end-effector pose | Obstacle Avoidance: extra DOF helps avoid collisions with obstacles or the robot itself | Singularity Avoidance: minimizes gimbal lock and improves motion flexibility in constrained environments | Enhanced Manipulability: allows better control over speed, strength, and precision | Expanded Workspace: 7-DOF arm can operate effectively in more complex environments compared to 6-DOF systems

#California wildfire | Challenges | Access roads too steep for fire department equipment | Brush fires | Dangerously strong winds for fire fighting planes | Drone interfering with wildfire response hit plane | Dry conditions fueled fires | Dry vegetation primed to burn | Faults on the power grid | Fires fueled by hurricane-force winds | Fire hydrants gone dry | Fast moving flames | Hilly areas | Increasing fire size, frequency, and susceptibility to beetle outbreaks and drought driven mortality | Keeping native biodiversity | Looting | Low water pressure | Managing forests, woodlands, shrublands, and grasslands for broad ecological and societal benefits | Power shutoffs | Ramping up security in areas that have been evacuated | Recoving the remains of people killed | Retardant drop pointless due to heavy winds | Smoke filled canyons | Santa Ana winds | Time it takes for water-dropping helicopter to arrive | Tree limbs hitting electrical wires | Use of air tankers is costly and increasingly ineffective | Utilities sensor network outdated | Water supply systems not built for wildfires on large scale | Wire fault causes a spark | Wires hitting one another | Assets | California National Guard | Curfews | Evacuation bags | Firefighters | Firefighting helicopter | Fire maps | Evacuation zones | Feeding centers | Heavy-lift helicopter | LiDAR technology to create detailed 3D maps of high-risk areas | LAFD (Los Angeles Fire Department) | Los Angeles County Sheriff Department | Los Angeles County Medical Examiner | National Oceanic and Atmospheric Administration | Recycled water irrigation reservoirs | Satellites for wildfire detection | Sensor network of LAFD | Smoke forecast | Statistics | Beachfront properties destroyed | Death tol | Damage | Economic losses | Expansion of non-native, invasive species | Loss of native vegetation | Structures (home, multifamily residence, outbuilding, vehicle) damaged | California wildfire actions | Animals relocated | Financial recovery programs | Efforts toward wildfire resilience | Evacuation orders | Evacuation warnings | Helicopters dropped water on evacuation routes to help residents escape | Reevaluating wildfire risk management | Schools closed | Schools to be inspected and cleaned outside and in, and their filters must be changed

#Quadruped robot | Lour-legged robot | Mimic animal locomotion | Navigate rough, uneven, cluttered terrains | Climb stairs | Operate indoors or outdoors

#Cognitive AI

#Athletic AI

#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

#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