“‘Robotics’ Tag”,2019-09-14 (AI scaling, tech economics, RL exploration, imitation learning, meta-learning, MARL, offline RL, RL scaling)⁠:

Bibliography for tag reinforcement-learning/robot, most recent first: 6 related tags, 286 annotations, & 77 links (parent).

• See Also
• Gwern
  • “Research Ideas”, Gwern2017
  • “Free-Play Periods for RL Agents”, Gwern2023
  • “ARPA and SCI: Surfing AI”, Gwern2018
• Links
  • “A Revolution in How Robots Learn”
  • “Data Scaling Laws in Imitation Learning for Robotic Manipulation”, Lin et al 2024
  • “Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making”, Li et al 2024
  • “Carpentopod: A Walking Table Project”, Carpentier2024
  • “New Data Hub Shows How Waymo Improves Road Safety”
  • “Motor Physics: Safety Implications of Geared Motors”, Jang2024
  • “Optically Actuated Soft Microrobot Family for Single-Cell Manipulation”, Iványi et al 2024
  • “Piecing Together the Secrets of the Stasi: After the Berlin Wall Fell, Agents of East Germany’s Secret Police Frantically Tore Apart Their Records. Archivists Have Spent the past 30 Years Trying to Restore Them”, Bilger2024
  • “VERO: A Vacuum-Cleaner-Equipped Quadruped Robot for Efficient Litter Removal”, Amatucci et al 2024
  • “Machine Learning Reveals the Control Mechanics of an Insect Wing Hinge”, Melis et al 2024
  • “Universal Chemical Programming Language for Robotic Synthesis Repeatability”, Rauschen et al 2024
  • “Mobile ALOHA: Learning Bimanual Mobile Manipulation With Low-Cost Whole-Body Teleoperation”, Fu et al 2024
  • “Robotic Microinjection Enables Large-Scale Transgenic Studies of Caenorhabditis Elegans”
  • “Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles”, Kusano et al 2023
  • “Mastering Stacking of Diverse Shapes With Large-Scale Iterative Reinforcement Learning on Real Robots”, Lampe et al 2023
  • “ReCoRe: Regularized Contrastive Representation Learning of World Model”, Poudel et al 2023
  • “Eureka: Human-Level Reward Design via Coding Large Language Models”, Ma et al 2023
  • “Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions”, Chebotar et al 2023
  • “Deep RL at Scale: Sorting Waste in Office Buildings With a Fleet of Mobile Manipulators”, Herzog et al 2023
  • “Learning Agile Soccer Skills for a Bipedal Robot With Deep Reinforcement Learning”, Haarnoja et al 2023
  • “ACT: Learning Fine-Grained Bimanual Manipulation With Low-Cost Hardware”, Zhao et al 2023
  • “LLM+P: Empowering Large Language Models With Optimal Planning Proficiency”, Liu et al 2023
  • “Bubble-Based Microrobots With Rapid Circular Motions for Epithelial Pinning and Drug Delivery”, Lee et al 2023
  • “ChemCrow: Augmenting Large-Language Models With Chemistry Tools”, Bran et al 2023
  • “Learning Humanoid Locomotion With Transformers”, Radosavovic et al 2023
  • “The Characteristics and Geographic Distribution of Robot Hubs in US Manufacturing Establishments”, Brynjolfsson et al 2023
  • “MimicPlay: Long-Horizon Imitation Learning by Watching Human Play”, Wang et al 2023
  • “Faithful Chain-Of-Thought Reasoning”, Lyu et al 2023
  • “Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Bronstein et al 2022
  • “Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes”, Kumar et al 2022
  • “Token Turing Machines”, Ryoo et al 2022
  • “Legged Locomotion in Challenging Terrains Using Egocentric Vision”, Agarwal et al 2022
  • “Broken Neural Scaling Laws”, Caballero et al 2022
  • “Creating a Dynamic Quadrupedal Robotic Goalkeeper With Reinforcement Learning”, Huang et al 2022
  • “DALL·E-Bot: Introducing Web-Scale Diffusion Models to Robotics”, Kapelyukh et al 2022
  • “Selective Neutralization and Deterring of Cockroaches With Laser Automated by Machine Vision”, Rakhmatulin et al 2022
  • “Versatile Articulated Aerial Robot DRAGON: Aerial Manipulation and Grasping by Vectorable Thrust Control”, Zhao et al 2022
  • “LaTTe: Language Trajectory TransformEr”, Bucker et al 2022
  • “PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations”, Lee et al 2022
  • “Semantic Abstraction (SemAbs): Open-World 3D Scene Understanding from 2D Vision-Language Models”, Ha & Song2022
  • “Inner Monologue: Embodied Reasoning through Planning With Language Models”, Huang et al 2022
  • “LM-Nav: Robotic Navigation With Large Pre-Trained Models of Language, Vision, and Action”, Shah et al 2022
  • “Watch and Match: Supercharging Imitation With Regularized Optimal Transport”, Haldar et al 2022
  • “Fleet-DAgger: Interactive Robot Fleet Learning With Scalable Human Supervision”, Hoque et al 2022
  • “DayDreamer: World Models for Physical Robot Learning”, Wu et al 2022
  • “ADAPT: Vision-Language Navigation With Modality-Aligned Action Prompts”, Lin et al 2022
  • “Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant et al 2022
  • “Gato: A Generalist Agent”, Reed et al 2022
  • “Rapid Locomotion via Reinforcement Learning”, Margolis et al 2022
  • “Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?”, Cui et al 2022
  • “Semantic Exploration from Language Abstractions and Pretrained Representations”, Tam et al 2022
  • “Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale”, Ramrakhya et al 2022
  • “Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning”, Valassakis et al 2022
  • “Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, Ahn et al 2022
  • “Socratic Models: Composing Zero-Shot Multimodal Reasoning With Language”, Zeng et al 2022
  • “CLIP on Wheels (CoW): Zero-Shot Object Navigation As Object Localization and Exploration”, Gadre et al 2022
  • “Robot Peels Banana With Goal-Conditioned Dual-Action Deep Imitation Learning”, Kim et al 2022
  • “SURF: Semi-Supervised Reward Learning With Data Augmentation for Feedback-Efficient Preference-Based Reinforcement Learning”, Park et al 2022
  • “The Unsurprising Effectiveness of Pre-Trained Vision Models for Control”, Parisi et al 2022
  • “VAPO: Affordance Learning from Play for Sample-Efficient Policy Learning”, Borja-Diaz et al 2022
  • “Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion”, Ji et al 2022
  • “LID: Pre-Trained Language Models for Interactive Decision-Making”, Li et al 2022
  • “Accelerated Quality-Diversity for Robotics through Massive Parallelism”, Lim et al 2022
  • “Surprisingly Robust In-Hand Manipulation: An Empirical Study”, Bhatt et al 2022
  • “Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots”, Bhatia et al 2022
  • “Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild”, Miki et al 2022
  • “Language Models As Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents”, Huang et al 2022
  • “Robots and Firm Investment”, Benmelech & Zator2022
  • “Agile Locomotion via Model-Free Learning”, Margolis2022
  • “Gastrointestinal Interoception in Eating Disorders: Charting a New Path”, Khalsa et al 2022
  • “Simple but Effective: CLIP Embeddings for Embodied AI”, Khandelwal et al 2021
  • “AW-Opt: Learning Robotic Skills With Imitation and Reinforcement at Scale”, Lu et al 2021
  • “BC-Z: Zero-Shot Task Generalization With Robotic Imitation Learning”, Jang et al 2021
  • “Learning Behaviors through Physics-Driven Latent Imagination”, Richard et al 2021
  • “Discovering and Achieving Goals via World Models”, Mendonca et al 2021
  • “Beyond Pick-And-Place: Tackling Robotic Stacking of Diverse Shapes”, Lee et al 2021
  • “Legged Robots That Keep on Learning: Fine-Tuning Locomotion Policies in the Real World”, Smith et al 2021
  • “Skill Induction and Planning With Latent Language”, Sharma et al 2021
  • “Bridge Data: Boosting Generalization of Robotic Skills With Cross-Domain Datasets”, Ebert et al 2021
  • “Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning”, Rudin et al 2021
  • “CLIPort: What and Where Pathways for Robotic Manipulation”, Shridhar et al 2021
  • “A Workflow for Offline Model-Free Robotic Reinforcement Learning”, Kumar et al 2021
  • “Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks”, Wu et al 2021
  • “Learning to Navigate Sidewalks in Outdoor Environments”, Sorokin et al 2021
  • “PlaTe: Visually-Grounded Planning With Transformers in Procedural Tasks”, Sun et al 2021
  • “Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation”, Nair et al 2021
  • “Implicit Behavioral Cloning”, Florence et al 2021
  • “Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning”, Makoviychuk et al 2021
  • “On the Opportunities and Risks of Foundation Models”, Bommasani et al 2021
  • “DexMV: Imitation Learning for Dexterous Manipulation from Human Videos”, Qin et al 2021
  • “Language Grounding With 3D Objects”, Thomason et al 2021
  • “Time-Optimal Planning for Quadrotor Waypoint Flight”, Foehn et al 2021
  • “RMA: Rapid Motor Adaptation for Legged Robots”, Kumar et al 2021
  • “Learning to See Before Learning to Act: Visual Pre-Training for Manipulation”, Yen-Chen et al 2021
  • “Coarse-To-Fine Q-Attention: Efficient Learning for Visual Robotic Manipulation via Discretisation”, James et al 2021
  • “The Rise of Intelligent Matter”, Kaspar et al 2021
  • “Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”, Riviere et al 2021
  • “Waymo Simulated Driving Behavior in Reconstructed Fatal Crashes within an Autonomous Vehicle Operating Domain”, Scanlon et al 2021
  • “Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing”, Brunnbauer et al 2021
  • “Replaying Real Life: How the Waymo Driver Avoids Fatal Human Crashes”, Waymo2021
  • “Asymmetric Self-Play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI et al 2021
  • “Go-Explore: First Return, Then Explore”, Ecoffet et al 2021
  • “Unlocking Pixels for Reinforcement Learning via Implicit Attention”, Choromanski et al 2021
  • “Regenerating Soft Robots through Neural Cellular Automata”, Horibe et al 2021
  • “MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics”, Nordmoen et al 2021
  • “ViNG: Learning Open-World Navigation With Visual Goals”, Shah et al 2020
  • “Learning Accurate Long-Term Dynamics for Model-Based Reinforcement Learning”, Lambert et al 2020
  • “A Framework for Efficient Robotic Manipulation”, Zhan et al 2020
  • “Imitating Interactive Intelligence”, Abramson et al 2020
  • “Autonomous Navigation of Stratospheric Balloons Using Reinforcement Learning”, Bellemare et al 2020
  • “A Recurrent Vision-And-Language BERT for Navigation”, Hong et al 2020
  • “MoGaze: A Dataset of Full-Body Motions That Includes Workspace Geometry and Eye-Gaze”, Kratzer et al 2020
  • “Multimodal Dynamics Modeling for Off-Road Autonomous Vehicles”, Tremblay et al 2020
  • “The Robot Household Marathon Experiment”, Kazhoyan et al 2020
  • “RetinaGAN: An Object-Aware Approach to Sim-To-Real Transfer”, Ho et al 2020
  • “Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding”, Roberts et al 2020
  • “MELD: Meta-Reinforcement Learning from Images via Latent State Models”, Zhao et al 2020
  • “Guys and Dolls”, Devlin & Locatelli2020
  • “DreamerV2: Mastering Atari With Discrete World Models”, Hafner et al 2020
  • “An Adaptive Deep Reinforcement Learning Framework Enables Curling Robots With Human-Like Performance in Real-World Conditions”, Won et al 2020
  • “Meta-Learning through Hebbian Plasticity in Random Networks”, Najarro & Risi2020
  • “Dm_control: Software and Tasks for Continuous Control”, Tassa et al 2020
  • “Sample Factory: Egocentric 3D Control from Pixels at 100,000 FPS With Asynchronous Reinforcement Learning”, Petrenko et al 2020
  • “Effectiveness of the Felixer Grooming Trap for the Control of Feral Cats: a Field Trial in Arid South Australia”, Moseby et al 2020
  • “Active Preference-Based Gaussian Process Regression for Reward Learning”, Bıyık et al 2020
  • “VLN-BERT: Improving Vision-And-Language Navigation With Image-Text Pairs from the Web”, Majumdar et al 2020
  • “First Return, Then Explore”, Ecoffet et al 2020
  • “AI-Powered Rat Could Be a Valuable New Tool for Neuroscience: Researchers from DeepMind and Harvard Are Using a Virtual Rat to See What Neural Networks Can Teach Us about Biology”, Gent2020
  • “Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning”, Julian et al 2020
  • “Learning Agile Robotic Locomotion Skills by Imitating Animals”, Peng et al 2020
  • “Learning to Fly via Deep Model-Based Reinforcement Learning”, Becker-Ehmck et al 2020
  • “Deep Neuroethology of a Virtual Rodent”, Merel et al 2020
  • “Learning to Walk in the Real World With Minimal Human Effort”, Ha et al 2020
  • “In the 1970s, the CIA Created a Robot Dragonfly Spy. Now We Know How It Works. Newly Released Documents Show How the CIA Created One of the World’s First Examples of Insect Robotics.”, Hambling2020
  • “The Messy, Secretive Reality behind OpenAI’s Bid to save the World: The AI Moonshot Was Founded in the Spirit of Transparency. This Is the inside Story of How Competitive Pressure Eroded That Idealism”, Hao2020
  • “Remote-Controlled Insect Navigation Using Plasmonic Nanotattoos”, Tadepalli et al 2020
  • “AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch”, Knight2020
  • “First-In-Human Evaluation of a Hand-Held Automated Venipuncture Device for Rapid Venous Blood Draws”, Leipheimer et al 2020
  • “Near-Perfect Point-Goal Navigation from 2.5 Billion Frames of Experience”, Wijmans & Kadian2020
  • “Reinforcement Learning Upside Down: Don’t Predict Rewards—Just Map Them to Actions”, Schmidhuber2019
  • “Increasing Generality in Machine Learning through Procedural Content Generation”, Risi & Togelius2019
  • “SwarmCloak: Landing of a Swarm of Nano-Quadrotors on Human Arms”, Tsykunov et al 2019
  • “Scaling Robot Supervision to Hundreds of Hours With RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity”, Mandlekar et al 2019
  • “DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames”, Wijmans et al 2019
  • “Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning”, Yu et al 2019
  • “Solving Rubik’s Cube With a Robot Hand”, OpenAI et al 2019
  • “Solving Rubik’s Cube With a Robot Hand [Blog]”, OpenAI 2019
  • “Scaling Data-Driven Robotics With Reward Sketching and Batch Reinforcement Learning”, Cabi et al 2019
  • “Learning to Seek: Autonomous Source Seeking With Deep Reinforcement Learning Onboard a Nano Drone Microcontroller”, Duisterhof et al 2019
  • “ROBEL: Robotics Benchmarks for Learning With Low-Cost Robots”, Ahn et al 2019
  • “TuneNet: One-Shot Residual Tuning for System Identification and Sim-To-Real Robot Task Transfer”, Allevato et al 2019
  • “The Robot Revolution: Managerial and Employment Consequences for Firms”, Dixon et al 2019
  • “An Application of Reinforcement Learning to Aerobatic Helicopter Flight”, Abbeel et al 2019
  • “AI-GAs: AI-Generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, Clune2019
  • “Adversarial Policies: Attacking Deep Reinforcement Learning”, Gleave et al 2019
  • “End-To-End Robotic Reinforcement Learning without Reward Engineering”, Singh et al 2019
  • “Habitat: A Platform for Embodied AI Research”, Savva et al 2019
  • “Target Specificity of the Felixer Grooming “trap””, Read et al 2019
  • “Long-Range Indoor Navigation With PRM-RL”, Francis et al 2019
  • “Learning Agile and Dynamic Motor Skills for Legged Robots”, Hwangbo et al 2019
  • “The RobotriX: An EXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences With Robot Trajectories and Interactions”, Garcia-Garcia et al 2019
  • “Living With Harmony: A Personal Companion System by Realbotix™”, Coursey et al 2019
  • “Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks”, James et al 2018
  • “ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst”, Bansal et al 2018
  • “Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control”, Ebert et al 2018
  • “An Introduction to Deep Reinforcement Learning”, Francois-Lavet et al 2018
  • “Organic Synthesis in a Modular Robotic System Driven by a Chemical Programming Language”, Steiner et al 2018
  • “Neural Probabilistic Motor Primitives for Humanoid Control”, Merel et al 2018
  • “Deep Reinforcement Learning”, Li2018
  • “Learning Navigation Behaviors End-To-End With AutoRL”, Chiang et al 2018
  • “Benchmarking Reinforcement Learning Algorithms on Real-World Robots”, Mahmood et al 2018
  • “Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios”, Fan et al 2018
  • “Learning Dexterous In-Hand Manipulation”, OpenAI et al 2018
  • “Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias”, Gupta et al 2018
  • “Visual Reinforcement Learning With Imagined Goals”, Nair et al 2018
  • “QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation”, Kalashnikov et al 2018
  • “Adversarial Active Exploration for Inverse Dynamics Model Learning”, Hong et al 2018
  • “More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Calandra et al 2018
  • “Learning Real-World Robot Policies by Dreaming”, Piergiovanni et al 2018
  • “Synthesizing Programs for Images Using Reinforced Adversarial Learning”, Ganin et al 2018
  • “One Big Net For Everything”, Schmidhuber2018
  • “One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning”, Yu et al 2018
  • “Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction”, Lathuilière et al 2017
  • “The Signature of Robot Action Success in EEG Signals of a Human Observer: Decoding and Visualization Using Deep Convolutional Neural Networks”, Behncke et al 2017
  • “Sim-To-Real Transfer of Robotic Control With Dynamics Randomization”, Peng et al 2017
  • “Flow: A Modular Learning Framework for Mixed Autonomy Traffic”, Wu et al 2017
  • “PRM-RL: Long-Range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning”, Faust et al 2017
  • “GraspGAN: Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping”, Bousmalis et al 2017
  • “Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics”, Chatzilygeroudis & Mouret2017
  • “One-Shot Visual Imitation Learning via Meta-Learning”, Finn et al 2017
  • “Brain Responses During Robot-Error Observation”, Welke et al 2017
  • “Deep Learning in Robotics: A Review of Recent Research”, Pierson & Gashler2017
  • “Proximal Policy Optimization Algorithms”, Schulman et al 2017
  • “The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously”, Cabi et al 2017
  • “Semi-Supervised Haptic Material Recognition for Robots Using Generative Adversarial Networks”, Erickson et al 2017
  • “Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”, Rahmatizadeh et al 2017
  • “Deep Q-Learning from Demonstrations”, Hester et al 2017
  • “Data-Efficient Deep Reinforcement Learning for Dexterous Manipulation”, Popov et al 2017
  • “Black-Box Data-Efficient Policy Search for Robotics”, Chatzilygeroudis et al 2017
  • “One-Shot Imitation Learning”, Duan et al 2017
  • “Enabling Robots to Communicate Their Objectives”, Huang et al 2017
  • “Sim-To-Real Robot Learning from Pixels With Progressive Nets”, Rusu et al 2016
  • “Supervision via Competition: Robot Adversaries for Learning Tasks”, Pinto et al 2016
  • “Deep Reinforcement Learning for Robotic Manipulation With Asynchronous Off-Policy Updates”, Gu et al 2016
  • “Deep Visual Foresight for Planning Robot Motion”, Finn & Levine2016
  • “Target-Driven Visual Navigation in Indoor Scenes Using Deep Reinforcement Learning”, Zhu et al 2016
  • “Learning Hand-Eye Coordination for Robotic Grasping With Deep Learning and Large-Scale Data Collection”, Levine et al 2016
  • “Optimization-Based Locomotion Planning, Estimation, and Control Design for the Atlas Humanoid Robot”, Kuindersma et al 2015
  • “MAP-Elites: Illuminating Search Spaces by Mapping Elites”, Mouret & Clune2015
  • “End-To-End Training of Deep Visuomotor Policies”, Levine et al 2015
  • “An Invitation to Imitation”, Bagnell2015
  • “Gaussian Processes for Data-Efficient Learning in Robotics and Control”, Deisenroth et al 2015
  • “Robots That Can Adapt like Animals”, Cully et al 2014
  • “PILCO: A Model-Based and Data-Efficient Approach to Policy Search”, Deisenroth & Rasmussen2011
  • “Motion Planning for Dynamic Folding of a Cloth With Two High-Speed Robot Hands and Two High-Speed Sliders”, Yamakawa et al 2011
  • “Towards Insect Cyborgs: Interfacing Microtechnologies With Metamorphic Development”, Bozkurt2010 (page 4)
  • “Motion Planning for Dynamic Knotting of a Flexible Rope With a High-Speed Robot Arm”, Yamakawa et al 2010
  • “Insect-Machine Interface Based Neurocybernetics”, Bozkurt et al 2009
  • “Weldon’s Dice, Automated”, Labby2009
  • “Resilient Machines Through Continuous Self-Modeling”, Bongard et al 2006
  • “Robot Predictions Evolution”, Moravec2004
  • “Spatio-Temporal Prediction Modulates the Perception of Self-Produced Stimuli”, Blakemore et al 1999
  • “When Will Computer Hardware Match the Human Brain?”, Moravec1998
  • “Evolving 3D Morphology and Behavior by Competition”, Sims1994
  • “The Stanford Cart and the CMU Rover”, Moravec1990
  • “Meet Shakey: the First Electronic Person—The Fascinating and Fearsome Reality of a Machine With a Mind of Its Own”, Darrach1970
  • “Automation Comes to More Factories With Robot Subscription Services”
  • “Visual Model-Based Reinforcement Learning As a Path towards Generalist Robots”
  • “Model-Based Reinforcement Learning from Pixels With Structured Latent Variable Models”
  • “End-To-End Deep Reinforcement Learning without Reward Engineering”
  • “Eric Jang”
  • “Learning to Write Programs That Generate Images”
  • “How Can We Make Robotics More like Generative Modeling?”
  • “Aurora’s Approach to Development”
  • “Why Testing Self-Driving Cars in SF Is Challenging but Necessary”
  • “Solving a Rubik’s Cube in Record Time”
  • “Learning Dexterity [Blog]”
  • “How Robots Can Acquire New Skills from Their Shared Experience”
  • “Scale: The Data Platform for AI; High Quality Training and Validation Data for AI Applications”, AI 2024
  • “Flippy the Fast Food Robot Just Got Hired in 100 Restaurants”
  • “AI-Guided Robots Are Ready to Sort Your Recyclables”
  • “It’s (Still) Really Hard for Robots to Autonomously Do Household Chores”
  • “How Boston Dynamics Taught Its Robots to Dance”
  • “Today’s Robotic Surgery Turns Surgical Trainees Into Spectators”
  • “Roomba Inventor Joe Jones on His New Weed-Killing Robot, and What’s So Hard About Consumer Robotics”
  • “Robotic AI Firm Covariant Raises Another $80 Million”
  • “OpenAI Disbands Its Robotics Research Team”
  • “The Universal Robot”
  • “Introducing Adept”
  • “Revamping the UPL’s People Counter”
  • “Domino Robot”
  • “Forget about Drones, Forget about Dystopian Sci-Fi—A Terrifying New Generation of Autonomous Weapons Is Already Here. Meet the Small Band of Dedicated Optimists Battling Nefarious Governments and Bureaucratic Tedium to Stop the Proliferation of Killer Robots And, Just Maybe, save Humanity from Itself.”
  • “Where Are The Robotic Bricklayers?”
  • “Economists Are Revising Their Views on Robots and Jobs”
  • “Multi-Modal Mobility Morphobot (M4) With Appendage Repurposing for Locomotion Plasticity Enhancement”
  • “The Rise of A.I. Fighter Pilots”
  • “When Self-Driving Cars Can’t Help Themselves, Who Takes the Wheel?”
  • “Inside Google’s Rebooted Robotics Program”
  • “The Robot Surgeon Will See You Now”
  • “This Robot Looks Like a Pancake and Jumps Like a Maggot”
  • “Energy Companies Turn to Robots to Install Solar Panels”
  • “A New Generation of AI-Powered Robots Is Taking over Warehouses”
  • “Japan Nuclear Plant Gets Help from US Robots: Obama Administration Sends Shipment of Robots to Help Regain Control over Stricken Fukushima Nuclear Plant”
  • “Alphabet Is Putting Its Prototype Robots to Work Cleaning up around Google’s Offices”
  • “Welcome to Simulation City, the Virtual World Where Waymo Tests Its Autonomous Vehicles”
  • “IRobot’s Newest Roomba Uses AI to Avoid Dog Poop”
  • “Autonomous Drones Could Soon Run the UK’s Energy Grid”
  • “An Oral History of the 2004 Darpa Grand Challenge”
  • “The Elusive Hunt for a Robot That Can Pick a Ripe Strawberry”
  • “This Brain-Controlled Robotic Arm Can Twist, Grasp—And Feel”
  • “Why Scientists Love Making Robots Build Ikea Furniture”
  • “The Robots Are Coming for Garment Workers. That’s Good for the U.S., Bad for Poor Countries: Automation Is Reaching into Trades That Once Seemed Immune, Transforming Sweatshops in Places like Bangladesh and Bringing Production back to America”
  • “Cats, Rats, A.I., Oh My!”
  • “SwarmCloak: Landing of a Swarm of Nano-Quadrotors on Human Arms [Video]”
  • “Spot’s Got an Arm!”
  • “Waymo 360° Experience: A Fully Autonomous Driving Journey”
  • “M4 Drives and Flies Around Caltech’s Campus”
  • “BRETT the Robot Learns to Put Things Together on His Own”
  • “Cuboth”
  • “Solving Rubik’s Cube With a Robot Hand: Perturbations”
  • “Target-Driven Visual Navigation in Indoor Scenes Using Deep Reinforcement Learning [Video]”
  • “How Waymo Is Making Roads Safer”
  • “48:44—Tesla Vision · 1:13:12—Planning and Control · 1:24:35—Manual Labeling · 1:28:11—Auto Labeling · 1:35:15—Simulation · 1:42:10—Hardware Integration · 1:45:40—Dojo”
  • “Robot Peels Banana With Deep Learning, UT ISI Lab”
  • “Supplementary Video for Do As I Can, Not As I Say: Grounding Language in Robotic Affordances”
  • “Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild [Video]”
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