What matters in learning from offline human demonstrations for robot manipulation
Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …
manipulation capabilities. While recent advances have been made in imitation learning and …
Coarse-to-fine imitation learning: Robot manipulation from a single demonstration
E Johns - 2021 IEEE international conference on robotics and …, 2021 - ieeexplore.ieee.org
We introduce a simple new method for visual imitation learning, which allows a novel robot
manipulation task to be learned from a single human demonstration, without requiring any …
manipulation task to be learned from a single human demonstration, without requiring any …
Human-in-the-loop imitation learning using remote teleoperation
Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …
reproducing behavior from human demonstrations. However, manipulation tasks often …
Learning to generalize across long-horizon tasks from human demonstrations
Imitation learning is an effective and safe technique to train robot policies in the real world
because it does not depend on an expensive random exploration process. However, due to …
because it does not depend on an expensive random exploration process. However, due to …
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 …
traditionally has exhibited limited scalability due to high data supervision requirements and …
Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …
sequence of motor actions. While deep reinforcement learning methods have recently …
Bridgedata v2: A dataset for robot learning at scale
We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …
Aw-opt: Learning robotic skills with imitation andreinforcement at scale
Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations,
or via reinforcement learning (RL) using large amounts of autonomously collected …
or via reinforcement learning (RL) using large amounts of autonomously collected …
Bottom-up skill discovery from unsegmented demonstrations for long-horizon robot manipulation
We tackle real-world long-horizon robot manipulation tasks through skill discovery. We
present a bottom-up approach to learning a library of reusable skills from unsegmented …
present a bottom-up approach to learning a library of reusable skills from unsegmented …
The ingredients of real-world robotic reinforcement learning
The success of reinforcement learning for real world robotics has been, in many cases
limited to instrumented laboratory scenarios, often requiring arduous human effort and …
limited to instrumented laboratory scenarios, often requiring arduous human effort and …