51 citations · 57 across the 9 of their papers we have counts for
10 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…
Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation
Kuancheng Wang, Vaibhav Saxena, Shuo Cheng +2
Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both search and act, and learning this…
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…
LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies
Yue Yang, Shuo Cheng, Yu Fang +4
General-purpose robots must master long-horizon manipulation, defined as tasks involving multiple kinematic structure changes (e.g., attaching or detaching objects) in unstructured…
Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training
Shuo Cheng, Liqian Ma, Zhenyang Chen +3
Behavior cloning has shown promise for robot manipulation, but real-world demonstrations are costly to acquire at scale. While simulated data offers a scalable alternative, particu…
EgoMimic: Scaling Imitation Learning via Egocentric Video
Simar Kareer, Dhruv Patel, Ryan Punamiya +5
The scale and diversity of demonstration data required for imitation learning is a significant challenge. We present EgoMimic, a full-stack framework which scales manipulation via…
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…
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…
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…