How to train your robot with deep reinforcement learning: lessons we have learned
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
Recent advances in robot learning from demonstration
H Ravichandar, AS Polydoros… - Annual review of …, 2020 - annualreviews.org
In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
Rt-2: Vision-language-action models transfer web knowledge to robotic control
A Brohan, N Brown, J Carbajal, Y Chebotar… - arXiv preprint arXiv …, 2023 - arxiv.org
We study how vision-language models trained on Internet-scale data can be incorporated
directly into end-to-end robotic control to boost generalization and enable emergent …
directly into end-to-end robotic control to boost generalization and enable emergent …
[HTML][HTML] Rt-2: Vision-language-action models transfer web knowledge to robotic control
We study how vision-language models trained on Internet-scale data can be incorporated
directly into end-to-end robotic control to boost generalization and enable emergent …
directly into end-to-end robotic control to boost generalization and enable emergent …
Bc-z: Zero-shot task generalization with robotic imitation learning
E Jang, A Irpan, M Khansari… - … on Robot Learning, 2022 - proceedings.mlr.press
In this paper, we study the problem of enabling a vision-based robotic manipulation system
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …
Prompting decision transformer for few-shot policy generalization
Human can leverage prior experience and learn novel tasks from a handful of
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …
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 …
Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation
Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …
robotics. However, most results focus on table-top manipulation, lacking the mobility and …
A survey of meta-reinforcement learning
While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …
machine learning, it is held back from more widespread adoption by its often poor data …
Instruction-driven history-aware policies for robotic manipulations
In human environments, robots are expected to accomplish a variety of manipulation tasks
given simple natural language instructions. Yet, robotic manipulation is extremely …
given simple natural language instructions. Yet, robotic manipulation is extremely …