8 papers
HIL: Hybrid Imitation Learning of Diverse Parkour Skills from Videos
Jiashun Wang, Yifeng Jiang, Haotian Zhang +4
Data-driven methods leveraging deep reinforcement learning have become the dominant paradigm for developing controllers that enable physically simulated characters to produce natur…
CRISP: Contact-Guided Real2Sim from Monocular Video with Planar Scene Primitives
Zihan Wang, Jiashun Wang, Jeff Tan +4
We introduce CRISP, a method that recovers simulatable human motion and scene geometry from monocular video. Prior work on joint human-scene reconstruction relies on data-driven pr…
Generalizing from References using a Multi-Task Reference and Goal-Driven RL Framework
Jiashun Wang, M. Eva Mungai, He Li +3
Learning agile humanoid behaviors from human motion offers a powerful route to natural, coordinated control, but existing approaches face a persistent trade-off: reference-tracking…
Learning Smooth Time-Varying Linear Policies with an Action Jacobian Penalty
Zhaoming Xie, Kevin Karol, Jessica Hodgins
Reinforcement learning provides a framework for learning control policies that can reproduce diverse motions for simulated characters. However, such policies often exploit unnatura…
ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control
Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu +25
Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittl…
Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
David Nguyen, Zulfiqar Zaidi, Kevin Karol +2
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robo…