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Found 24 papers, 7 papers with code
Voyager: An Open-Ended Embodied Agent with Large Language Models
We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention.
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
Autonomous agents have made great strides in specialist domains like Atari games and Go.
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine.
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim.
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Based on the study, we derive a series of lessons including the sensitivity to different algorithmic design choices, the dependence on the quality of the demonstrations, and the variability based on the stopping criteria due to the different objectives in training and evaluation.
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency.
MoCoDA: Model-based Counterfactual Data Augmentation
To this end, we show that (1) known local structure in the environment transitions is sufficient for an exponential reduction in the sample complexity of training a dynamics model, and (2) a locally factored dynamics model provably generalizes out-of-distribution to unseen states and actions.
RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation
Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification.
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity
We evaluate the quality of our platform, the diversity of demonstrations in our dataset, and the utility of our dataset via quantitative and qualitative analysis.
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task.
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
In the second stage of GTI, we collect a small set of rollouts from the unconditioned stochastic policy of the first stage, and train a goal-directed agent to generalize to novel start and goal configurations.
Controlling Assistive Robots with Learned Latent Actions
Our insight is that we can make assistive robots easier for humans to control by leveraging latent actions.
Robotics
Human-in-the-Loop Imitation Learning using Remote Teleoperation
We develop a simple and effective algorithm to train the policy iteratively on new data collected by the system that encourages the policy to learn how to traverse bottlenecks through the interventions.
Learning Multi-Arm Manipulation Through Collaborative Teleoperation
To address these challenges, we present Multi-Arm RoboTurk (MART), a multi-user data collection platform that allows multiple remote users to simultaneously teleoperate a set of robotic arms and collect demonstrations for multi-arm tasks.
Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control
Key to such capability is hand-eye coordination, a cognitive ability that enables humans to adaptively direct their movements at task-relevant objects and be invariant to the objects' absolute spatial location.
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning
Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment.
Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation
Doing this is challenging for two reasons: on the data side, current interfaces make collecting high-quality human demonstrations difficult, and on the learning side, policies trained on limited data can suffer from covariate shift when deployed.
Learning and Retrieval from Prior Data for Skill-based Imitation Learning
Imitation learning offers a promising path for robots to learn general-purpose behaviors, but traditionally has exhibited limited scalability due to high data supervision requirements and brittle generalization.
Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks
ATR selects suitable tasks, which consist of an initial environment state and manipulation goal, for learning robust skills by balancing the diversity and feasibility of the tasks.
Imitating Task and Motion Planning with Visuomotor Transformers
In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation.
Human-in-the-Loop Task and Motion Planning for Imitation Learning
Finally, we collected 2. 1K demos with HITL-TAMP across 12 contact-rich, long-horizon tasks and show that the system often produces near-perfect agents.
MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.
NOD-TAMP: Multi-Step Manipulation Planning with Neural Object Descriptors
Developing intelligent robots for complex manipulation tasks in household and factory settings remains challenging due to long-horizon tasks, contact-rich manipulation, and the need to generalize across a wide variety of object shapes and scene layouts.
Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following
As a specific case of our framework, we devise a model predictive control method for path tracking.
