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Found 13 papers, 3 papers with code
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.
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.
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.
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.
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.
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine.
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.
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.
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.
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
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.
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.
