8 citations · 11 across the 8 of their papers we have counts for
12 papers
HLO-AMP: Hierarchical Loco-Manipulation from Object-Aware Motion Priors
Jean-Pierre Sleiman, Bilal Hammoud, He Li +4
Hierarchical pipelines for humanoid loco-manipulation typically pair a task-agnostic whole-body controller (WBC) with a downstream task policy. Such WBCs are often trained through…
A Compositional Perspective on Communication-Control Co-Design for Mobile Broadband Systems Beyond 6G
Angelo Vera-Rivera, Atefeh Termehchi, Ekram Hossain
Anticipated applications of beyond sixth-generation (B6G) mobile broadband networks will require the co-design of communication and control subsystems within the network architectu…
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…
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…
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…
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…
Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control
Xiaoyu Huang, Takara Truong, Yunbo Zhang +5
We present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation. While exis…
ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills
Tairan He, Jiawei Gao, Wenli Xiao +15
Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a s…
Strategy and Skill Learning for Physics-based Table Tennis Animation
Jiashun Wang, Jessica Hodgins, Jungdam Won
Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, bo…
SMPLOlympics: Sports Environments for Physically Simulated Humanoids
Zhengyi Luo, Jiashun Wang, Kangni Liu +9
We present SMPLOlympics, a collection of physically simulated environments that allow humanoids to compete in a variety of Olympic sports. Sports simulation offers a rich and stand…