Octo: An open-source generalist robot policy

OM Team, D Ghosh, H Walke, K Pertsch… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Large policies pretrained on diverse robot datasets have the potential to transform robotic
learning: instead of training new policies from scratch, such generalist robot policies may be …

Robot learning in the era of foundation models: A survey

X **ao, J Liu, Z Wang, Y Zhou, Y Qi, Q Cheng… - arxiv preprint arxiv …, 2023‏ - arxiv.org
The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

Cyberdemo: Augmenting simulated human demonstration for real-world dexterous manipulation

J Wang, Y Qin, K Kuang, Y Korkmaz… - Proceedings of the …, 2024‏ - openaccess.thecvf.com
We introduce CyberDemo a novel approach to robotic imitation learning that leverages
simulated human demonstrations for real-world tasks. By incorporating extensive data …

Robocasa: Large-scale simulation of everyday tasks for generalist robots

S Nasiriany, A Maddukuri, L Zhang, A Parikh… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In
Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate …

Robotgpt: Robot manipulation learning from chatgpt

Y **, D Li, A Yong, J Shi, P Hao, F Sun… - IEEE Robotics and …, 2024‏ - ieeexplore.ieee.org
We present RobotGPT, an innovative decision framework for robotic manipulation that
prioritizes stability and safety. The execution code generated by ChatGPT cannot guarantee …

: A Vision-Language-Action Flow Model for General Robot Control

K Black, N Brown, D Driess, A Esmail, M Equi… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and
dexterous robot systems, as well as to address some of the deepest questions in artificial …

Transic: Sim-to-real policy transfer by learning from online correction

Y Jiang, C Wang, R Zhang, J Wu, L Fei-Fei - arxiv preprint arxiv …, 2024‏ - arxiv.org
Learning in simulation and transferring the learned policy to the real world has the potential
to enable generalist robots. The key challenge of this approach is to address simulation-to …

The colosseum: A benchmark for evaluating generalization for robotic manipulation

W Pumacay, I Singh, J Duan, R Krishna… - arxiv preprint arxiv …, 2024‏ - arxiv.org
To realize effective large-scale, real-world robotic applications, we must evaluate how well
our robot policies adapt to changes in environmental conditions. Unfortunately, a majority of …

Echoscene: Indoor scene generation via information echo over scene graph diffusion

G Zhai, EP Örnek, DZ Chen, R Liao, Y Di… - … on Computer Vision, 2024‏ - Springer
We present EchoScene, an interactive and controllable generative model that generates 3D
indoor scenes on scene graphs. EchoScene leverages a dual-branch diffusion model that …

Human-in-the-loop task and motion planning for imitation learning

A Mandlekar, CR Garrett, D Xu… - Conference on Robot …, 2023‏ - proceedings.mlr.press
Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …