1 citations · 1 across the 6 of their papers we have counts for
6 papers
ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation
Yangcen Liu, Woo Chul Shin, Yunhai Han +3
Learning robot manipulation from abundant human videos offers a scalable alternative to costly robot-specific data collection. However, domain gaps across visual, morphological, an…
On the Surprising Effectiveness of Spectral Clipping in Learning Stable Linear and Latent-Linear Dynamical Systems
Hanyao Guo, Yunhai Han, Harish Ravichandar
When learning stable linear dynamical systems from data, three important properties are desirable: i) predictive accuracy, ii) verifiable stability, and iii) computational efficien…
AsymDex: Asymmetry and Relative Coordinates for RL-based Bimanual Dexterity
Zhaodong Yang, Yunhai Han, Ai-Ping Hu +1
We present Asymmetric Dexterity (AsymDex), a novel and simple reinforcement learning (RL) framework that can efficiently learn a large class of bimanual skills in multi-fingered ha…
Safe Navigation of Bipedal Robots via Koopman Operator-Based Model Predictive Control
Jeonghwan Kim, Yunhai Han, Harish Ravichandar +1
Nonlinearity in dynamics has long been a major challenge in robotics, often causing significant performance degradation in existing control algorithms. For example, the navigation…
KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation
Hongyi Chen, Abulikemu Abuduweili, Aviral Agrawal +4
Learning dexterous manipulation skills presents significant challenges due to complex nonlinear dynamics that underlie the interactions between objects and multi-fingered hands. Ko…
Learning Prehensile Dexterity by Imitating and Emulating State-only Observations
Yunhai Han, Zhenyang Chen, Kyle A Williams +1
When human acquire physical skills (e.g., tennis) from experts, we tend to first learn from merely observing the expert. But this is often insufficient. We then engage in practice,…