A review of robot learning for manipulation: Challenges, representations, and algorithms
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …
interacting with the world around them to achieve their goals. The last decade has seen …
Robocat: A self-improving generalist agent for robotic manipulation
The ability to leverage heterogeneous robotic experience from different robots and tasks to
quickly master novel skills and embodiments has the potential to transform robot learning …
quickly master novel skills and embodiments has the potential to transform robot learning …
Toward next-generation learned robot manipulation
The ever-changing nature of human environments presents great challenges to robot
manipulation. Objects that robots must manipulate vary in shape, weight, and configuration …
manipulation. Objects that robots must manipulate vary in shape, weight, and configuration …
Robustness via retrying: Closed-loop robotic manipulation with self-supervised learning
Prediction is an appealing objective for self-supervised learning of behavioral skills,
particularly for autonomous robots. However, effectively utilizing predictive models for …
particularly for autonomous robots. However, effectively utilizing predictive models for …
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 …
Language-conditioned imitation learning for robot manipulation tasks
Imitation learning is a popular approach for teaching motor skills to robots. However, most
approaches focus on extracting policy parameters from execution traces alone (ie, motion …
approaches focus on extracting policy parameters from execution traces alone (ie, motion …
Robocat: A self-improving foundation agent for robotic manipulation
The ability to leverage heterogeneous robotic experience from different robots and tasks to
quickly master novel skills and embodiments has the potential to transform robot learning …
quickly master novel skills and embodiments has the potential to transform robot learning …
Q-attention: Enabling efficient learning for vision-based robotic manipulation
S James, AJ Davison - IEEE Robotics and Automation Letters, 2022 - ieeexplore.ieee.org
Despite the success of reinforcement learning methods, they have yet to have their
breakthrough moment when applied to a broad range of robotic manipulation tasks. This is …
breakthrough moment when applied to a broad range of robotic manipulation tasks. This is …
The foundation of efficient robot learning
LP Kaelbling - Science, 2020 - science.org
The past 10 years have seen enormous breakthroughs in machine learning, resulting in
game-changing applications in computer vision and language processing. The field of …
game-changing applications in computer vision and language processing. The field of …
Model learning for robot control: a survey
D Nguyen-Tuong, J Peters - Cognitive processing, 2011 - Springer
Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …
dynamics models of the robot's own body and controllable external objects. It is widely …