112 citations · 304 across the 149 of their papers we have counts for
10 papers · 2 filters
Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies
Zixuan Chen, Xialin He, Yen-Jen Wang +8
Reinforcement learning combined with sim-to-real transfer offers a general framework for developing locomotion controllers for legged robots. To facilitate successful deployment in…
Automated Creation of Digital Cousins for Robust Policy Learning
Tianyuan Dai, Josiah Wong, Yunfan Jiang +5
Training robot policies in the real world can be unsafe, costly, and difficult to scale. Simulation serves as an inexpensive and potentially limitless source of training data, but…
Generalizable Humanoid Manipulation with 3D Diffusion Policies
Yanjie Ze, Zixuan Chen, Wenhao Wang +5
Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been…
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
Stephen Tian, Blake Wulfe, Kyle Sargent +4
Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, envi…
RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing
Bo Ai, Stephen Tian, Haochen Shi +4
Tactile feedback is critical for understanding the dynamics of both rigid and deformable objects in many manipulation tasks, such as non-prehensile manipulation and dense packing.…
Evaluating Real-World Robot Manipulation Policies in Simulation
Xuanlin Li, Kyle Hsu, Jiayuan Gu +13
The field of robotics has made significant advances towards generalist robot manipulation policies. However, real-world evaluation of such policies is not scalable and faces reprod…
Composable Part-Based Manipulation
Weiyu Liu, Jiayuan Mao, Joy Hsu +3
In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and…
Learning Planning Abstractions from Language
Weiyu Liu, Geng Chen, Joy Hsu +2
This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning abstraction from language (PARL), utilizes…
TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
Yunfan Jiang, Chen Wang, Ruohan Zhang +2
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 simulat…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…