3 citations · 11 across the 21 of their papers we have counts for
21 papers
ASPIRE: Agentic /Skills Discovery for Robotics
Runyu Lu, Yubo Wu, Ethan Kou +13
Traditional robot programming is notoriously challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse environment conf…
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
Wenli Xiao, Jia Xie, Tonghe Zhang +14
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of gener…
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
Letian Fu, Justin Yu, Karim El-Refai +13
"Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied ma…
Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer
Haoru Xue, Tairan He, Zi Wang +9
Recent progress in GPU-accelerated, photorealistic simulation has opened a scalable data-generation path for robot learning, where massive physics and visual randomization allow po…
VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation
Tairan He, Zi Wang, Haoru Xue +11
A key barrier to the real-world deployment of humanoid robots is the lack of autonomous loco-manipulation skills. We introduce VIRAL, a visual sim-to-real framework that learns hum…
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Zhengyi Luo, Ye Yuan, Tingwu Wang +25
Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid contro…
Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
Wenli Xiao, Haotian Lin, Andy Peng +9
Supervised fine-tuning (SFT) has become the de facto post-training strategy for large vision-language-action (VLA) models, but its reliance on costly human demonstrations limits sc…
Hold My Beer: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control
Yitang Li, Yuanhang Zhang, Wenli Xiao +5
Can your humanoid walk up and hand you a full cup of beer, without spilling a drop? While humanoids are increasingly featured in flashy demos like dancing, delivering packages, tra…
Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning
Zhengyi Luo, Chen Tessler, Toru Lin +8
Human behavior is fundamentally shaped by visual perception -- our ability to interact with the world depends on actively gathering relevant information and adapting our movements…
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…
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…
Agile Mobility with Rapid Online Adaptation via Meta-learning and Uncertainty-aware MPPI
Dvij Kalaria, Haoru Xue, Wenli Xiao +3
Modern non-linear model-based controllers require an accurate physics model and model parameters to be able to control mobile robots at their limits. Also, due to surface slipping…
AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility
Wenli Xiao, Haoru Xue, Tony Tao +3
Recent works in the robot learning community have successfully introduced generalist models capable of controlling various robot embodiments across a wide range of tasks, such as n…
OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
Tairan He, Zhengyi Luo, Xialin He +6
We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, Omni…
WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts
Chong Zhang, Wenli Xiao, Tairan He +1
Humanoid activities involving sequential contacts are crucial for complex robotic interactions and operations in the real world and are traditionally solved by model-based motion p…
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
Tairan He, Zhengyi Luo, Wenli Xiao +4
We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB ca…
Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
Tairan He, Chong Zhang, Wenli Xiao +3
Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans. Existing studies either de…
Safe Deep Policy Adaptation
Wenli Xiao, Tairan He, John Dolan +1
A critical goal of autonomy and artificial intelligence is enabling autonomous robots to rapidly adapt in dynamic and uncertain environments. Classic adaptive control and safe cont…
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning
Wenli Xiao, Yiwei Lyu, John Dolan
Multi-Agent Reinforcement Learning (MARL) discovers policies that maximize reward but do not have safety guarantees during the learning and deployment phases. Although shielding wi…
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Bing Luo, Wenli Xiao, Shiqiang Wang +2
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communicatio…