51 citations · 59 across the 9 of their papers we have counts for
9 papers
Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
Shuo Cheng, Chuye Zhang, Alfred Cueva +3
Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions. However, transferring human demonstra…
EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
Yangcen Liu, Shuo Cheng, Xinchen Yin +6
Dexterous manipulation is limited by the cost of collecting large-scale robot demonstrations. Egocentric human videos offer a scalable source of diverse manipulation behaviors, but…
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige +5
Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent o…
A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches
Zhigen Zhao, Shuo Cheng, Yan Ding +4
Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic…
Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models
Utkarsh A. Mishra, Shangjie Xue, Yongxin Chen +1
Long-horizon tasks, usually characterized by complex subtask dependencies, present a significant challenge in manipulation planning. Skill chaining is a practical approach to solvi…
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…