103 citations · 217 across the 16 of their papers we have counts for
17 papers
Who Improves the Robot? Toward Physical Recursive Self-Improvement
Hai Zhou, Fan Yang, Zeyi Chen +27
Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Self-Improvement (Physical RSI)…
RoboStriker: Latent-Space Strategic Games for Autonomous Humanoid Boxing
Kangning Yin, Kaige Liu, Zhe Cao +9
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a profound challenge, particularly in contact-rich and highly dynamic tasks such as b…
Scaling Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Kangning Yin, Xiaojie Niu +15
Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making i…
A Definition and Roadmap for World Models
Xinyuan Chen, Haoyu Guo, Shi Guo +10
World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI. From model-based reinfor…
DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization
Sixu Lin, Yunpeng Qing, Litao Liu +4
Recent progress in Reinforcement Learning (RL) provides a principled approach to optimizing Vision-Language-Action (VLA) models, facilitating a shift from trajectory imitation to a…
PersonaArena: Dynamic Simulation for Evaluating and Enhancing Persona-Level Role-Playing in Large Language Models
Wenlong Shi, Jianxun Lian, Mingqi Wu +5
Large language models (LLMs) increasingly serve as interactive social agents, yet their ability to maintain coherent and authentic persona-level role-playing remains limited, parti…
Scalable and General Whole-Body Control for Cross-Humanoid Locomotion
Yufei Xue, YunFeng Lin, Wentao Dong +6
Learning-based whole-body controllers have become a key driver for humanoid robots, yet most existing approaches require robot-specific training. In this paper, we study the proble…
RoboStriker: Hierarchical Decision-Making for Autonomous Humanoid Boxing
Kangning Yin, Zhe Cao, Wentao Dong +7
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a major challenge, particularly in contact-rich and highly dynamic tasks such as boxi…
H-Zero: Cross-Humanoid Locomotion Pretraining Enables Few-shot Novel Embodiment Transfer
Yunfeng Lin, Minghuan Liu, Yufei Xue +4
The rapid advancement of humanoid robotics has intensified the need for robust and adaptable controllers to enable stable and efficient locomotion across diverse platforms. However…
Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging
Mingyan Zhou, Biao Wang, Tian Tan +1
In autonomous driving, end-to-end methods utilizing Imitation Learning (IL) and Reinforcement Learning (RL) are becoming more and more common. However, they do not involve explicit…
Efficient Policy Space Response Oracles
Ming Zhou, Jingxiao Chen, Ying Wen +4
Policy Space Response Oracle methods (PSRO) provide a general solution to learn Nash equilibrium in two-player zero-sum games but suffer from two drawbacks: (1) the computation ine…
Generative Adversarial Exploration for Reinforcement Learning
Weijun Hong, Menghui Zhu, Minghuan Liu +4
Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses o…
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang +6
Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated se…
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts
Weinan Zhang, Xihuai Wang, Jian Shen +1
This paper investigates the model-based methods in multi-agent reinforcement learning (MARL). We specify the dynamics sample complexity and the opponent sample complexity in MARL,…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…
Multi-Agent Interactions Modeling with Correlated Policies
Minghuan Liu, Ming Zhou, Weinan Zhang +4
In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents. However, previous work on modeling multi-agent interactions from demonstratio…
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Ming Zhou, Jiarui Jin, Weinan Zhang +6
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems. Most of the existing solutions for order-dispatching are centralize…