activity
20192026
most citedSMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

103 citations · 209 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2025

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…