51 citations · 59 across the 9 of their papers we have counts for
4 papers · 1 filter
NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors
Shuo Cheng, Caelan Garrett, Ajay Mandlekar +1
Solving complex manipulation tasks in household and factory settings remains challenging due to long-horizon reasoning, fine-grained interactions, and broad object and scene divers…
Neural Field Dynamics Model for Granular Object Piles Manipulation
Shangjie Xue, Shuo Cheng, Pujith Kachana +1
We present a learning-based dynamics model for granular material manipulation. Inspired by the Eulerian approach commonly used in fluid dynamics, our method adopts a fully convolut…
Human-in-the-Loop Task and Motion Planning for Imitation Learning
Ajay Mandlekar, Caelan Garrett, Danfei Xu +1
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) s…
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
Sachit Kuhar, Shuo Cheng, Shivang Chopra +2
Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected…