7 citations · 16 across the 12 of their papers we have counts for
8 papers · 1 filter
LEGATO: Cross-Embodiment Imitation Using a Grasping Tool
Mingyo Seo, H. Andy Park, Shenli Yuan +2
Cross-embodiment imitation learning enables policies trained on specific embodiments to transfer across different robots, unlocking the potential for large-scale imitation learning…
SPOT: SE(3) Pose Trajectory Diffusion for Object-Centric Manipulation
Cheng-Chun Hsu, Bowen Wen, Jie Xu +5
We introduce SPOT, an object-centric imitation learning framework. The key idea is to capture each task by an object-centric representation, specifically the SE(3) object pose traj…
Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions
Zhenyu Jiang, Yuqi Xie, Jinhan Li +3
Humanoid robots, with their human-like embodiment, have the potential to integrate seamlessly into human environments. Critical to their coexistence and cooperation with humans is…
OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation
Jinhan Li, Yifeng Zhu, Yuqi Xie +4
We study the problem of teaching humanoid robots manipulation skills by imitating from single video demonstrations. We introduce OKAMI, a method that generates a manipulation plan…
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
Zhenyu Jiang, Yuqi Xie, Kevin Lin +5
Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more br…
HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots
Tairan He, Wenli Xiao, Toru Lin +9
Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-manipulation, and tabletop manipulation, each demanding a different mode of control. For exa…
KinScene: Model-Based Mobile Manipulation of Articulated Scenes
Cheng-Chun Hsu, Ben Abbatematteo, Zhenyu Jiang +3
Sequentially interacting with articulated objects is crucial for a mobile manipulator to operate effectively in everyday environments. To enable long-horizon tasks involving articu…
PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation
Mingyo Seo, Yoonyoung Cho, Yoonchang Sung +3
We introduce a learning-guided motion planning framework that generates seed trajectories using a diffusion model for trajectory optimization. Given a workspace, our method approxi…