autonomousrobot.app

#Autonomous Robot Application Meta

#Industrial inspection robots | Mobile data gathering systems | Carry measurement sensors | Designed to navigate facilities built for humans | Autonomous mobile inspection robots offer scalable and effective method for continuously collecting and analyzing digital operational data | Improve safety by removing human workers from hazardous environments

#Cobot | Collaborative Robot | Robot Arms

#Drilling robot | Underground mining operations to drill holes for blasting or exploration in mining

#Exploration Robot | Mapping and prospecting for mining

#Data Acquisition Robot

#Manipulation Robot

#Manufactoring Robot

#Mobile Robot

#Robot Arm

#Robotic Control System

#Robotic Inspection

#Robot Manoeuvre

#Remote Mining Vehicle

#Sensing

#Thinking

#Cognition

#Actuation

#Mobility

#Robot Vision

#Motion Control | Precision motion control

#Autonomous Mobile Robot

#Logistics Efficiency Improving

#Material Handling Workflow

#Robot Delivery Efficiency

#Robot Efficiency

#Robot Flexibility

#Robot Scalability

#Robot Speed

#Robot Integration

#Warehouse Technology

#Material Handling

#Autonomous Mobile Robot | AMR | Warehouse product handling, tracking, and movement

#Motion Control Solutions for Robotics

#Autonomous Security Robot | ASR

#Automated Gunshot Detection | AGD

#Machine-as-a-Service | MaaS

#Autonomous navigation both outdoors and indoors

#Computer Vision and Video Analytics

#Thermal Imaging and Emotion Detection

#Acoustic Event Detection and Machine Listening

#Threat Detection

#Loading within warehouses

#Unloading within warehouses

#Palletizing within warehouses

#Depalletizing within warehouses

#Sorting and Staging within warehouses

#Packing within warehouses

#Unpacking within warehouses

#Computer Vision

#3D Perception

#Unknown environment

#Unstructured environment

#Reinforcement learning

#Robotic manipulation

#Decision making under partial observability

#Imitation learning

#Decision making in multi-agent systems

#Mission planning

#Scheduling

#Healthcare Robotics

#Surgical Systems

#Interventional Systems

#Disinfection and sterilization

#Human augmentation

#Rehabilitation

#Quality-of-life enablement

#Strain wave gear technology

#SLAM | Simultaneous Localization and Mapping

#Learning Management System (LMS)

#Time To First Token (TTFT)

#Robotic Magnetic Navigation

#Humans in the loop

#Obtaining labeled data for AI models

#Building foundation models

#Robot manipulator

#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

#Robots-as-a-Service (RaaS) | Gives companies option to hire robots rather than purchase them outright, lowering financial risk while still providing full benefits of automation | Fixed monthly fee for fleet of robots to perform tasks | Robots are flexible workforce that can be hired on demand

#Agile mobile robots

#Cutting-edge solutions | Safely access hazardous or remote areas with ease | Enable predictive maintenance to reduce unplanned downtime | Improve operational visibility to enhance efficiency and reliability | Automate repetitive, labor-intensive inspection tasks

#Pick & Place | automatic picking and placing of parts or components

#Material Handling | safe and precise handling of materials of various weights and sizes

#Automated Transfer System | intelligent transfer between stations or production lines

#Line Feeding | continuous feeding of assembly or production lines

#Line Evacuation/Offloading | evacuation of finished products or subassemblies

#Material Flow Automation | automated management of material flow within the plant

#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

#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

#Transport conveyor systems | Mobile-robotics

#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