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 …
Train offline, test online: A real robot learning benchmark
Three challenges limit the progress of robot learning research: robots are expensive (few
labs can participate), everyone uses different robots (findings do not generalize across labs) …
labs can participate), everyone uses different robots (findings do not generalize across labs) …
Data augmentation for manipulation
P Mitrano, D Berenson - arXiv preprint arXiv:2205.02886, 2022 - arxiv.org
The success of deep learning depends heavily on the availability of large datasets, but in
robotic manipulation there are many learning problems for which such datasets do not exist …
robotic manipulation there are many learning problems for which such datasets do not exist …
Scaling data-driven robotics with reward sketching and batch reinforcement learning
We present a framework for data-driven robotics that makes use of a large dataset of
recorded robot experience and scales to several tasks using learned reward functions. We …
recorded robot experience and scales to several tasks using learned reward functions. We …
[引用][C] A framework for data-driven robotics
Bulletarm: An open-source robotic manipulation benchmark and learning framework
We present BulletArm, a novel benchmark and learning-environment for robotic
manipulation. BulletArm is designed around two key principles: reproducibility and …
manipulation. BulletArm is designed around two key principles: reproducibility and …
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 …
Benchmarking structured policies and policy optimization for real-world dexterous object manipulation
Dexterous manipulation is a challenging and important problem in robotics. While data-
driven methods are a promising approach, current benchmarks require simulation or …
driven methods are a promising approach, current benchmarks require simulation or …