6 papers
GRAFT: Graph-Based Affordance Transfer via Part Correspondence
Mengying Lin, Utkarsh Mishra, Ajay Mandlekar +1
Generalizing robotic manipulation to unseen objects remains challenging, as learning-based approaches require many demonstrations and fail in few-shot settings. Prior work transfer…
KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
Yixuan Huang, Bowen Li, Vaibhav Saxena +9
Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand.…
EgoAVFlow: Robot Policy Learning with Active Vision from Human Egocentric Videos via 3D Flow
Daesol Cho, Youngseok Jang, Danfei Xu +1
Egocentric human videos provide a scalable source of manipulation demonstrations; however, deploying them on robots requires active viewpoint control to maintain task-critical visi…
Reference Grounded Skill Discovery
Seungeun Rho, Aaron Trinh, Danfei Xu +1
Scaling unsupervised skill discovery algorithms to high-DoF agents remains challenging. As dimensionality increases, the exploration space grows exponentially, while the manifold o…
EMMA: Scaling Mobile Manipulation via Egocentric Human Data
Lawrence Y. Zhu, Pranav Kuppili, Ryan Punamiya +5
Scaling mobile manipulation imitation learning is bottlenecked by expensive mobile robot teleoperation. We present Egocentric Mobile MAnipulation (EMMA), an end-to-end framework tr…
Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation
Fukang Liu, Zhaoyuan Gu, Yilin Cai +8
Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex c…