59 citations · 104 across the 15 of their papers we have counts for
4 papers · 1 filter
One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs
Jianren Wang, Kangni Liu, Dingkun Guo +2
Learning to manipulate dynamic and deformable objects from a single demonstration video holds great promise in terms of scalability. Previous approaches have predominantly focused…
Thin-Shell Object Manipulations With Differentiable Physics Simulations
Yian Wang, Juntian Zheng, Zhehuan Chen +4
In this work, we aim to teach robots to manipulate various thin-shell materials. Prior works studying thin-shell object manipulation mostly rely on heuristic policies or learn poli…
DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation
Zilin Si, Gu Zhang, Qingwei Ben +4
We introduce DIFFTACTILE, a physics-based differentiable tactile simulation system designed to enhance robotic manipulation with dense and physically accurate tactile feedback. In…
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Yufei Wang, Zhanyi Sun, Jesse Zhang +4
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to desi…