Humanoid soccer contact skills require more than producing high-impact foot-ball contacts: the robot must close the loop over perception, approach, alignment, impact, and recovery while its own motion induces substantial viewpoint changes, frequent loss of the ball from view, and uncertain contact outcomes. In this wor...
Jia-Kang Jin, Yi-Xiao Huo, Peng-Yuan Wang et al.· 0 citations
SkillX is presented, a unified reinforcement learning framework that learns and composes multiple atomic soccer skills through a single command-conditioned policy, enabling the robot to execute atomic skills and transition among them such as dribbling, trapping, and shooting.
Zhang-Chen Ye, En-Xuan Ruan, Yi-Fei Bao et al.· 2 citations
Vision-Language-Action models and World-Action Models have advanced language-conditioned robotic manipulation, yet often leave metric relations among actions, objects, and scene geometry implicit. Human manipulation combines semantic understanding of task-relevant objects with spatial feedback that guides hand motion r...
Li-Jie Wang, Zheng Lu, Yi-Ming Wang et al.· 0 citations
We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each stage of the reinforcement-learning (RL) training loop around a single principle: components should be verified, clean, and customizable. With accuracy, efficiency, reliab...
To achieve effective, stealthy, and persistent control, TrojanWorld combines Decision-Reflective Induction to steer trigger-conditioned imagination toward attacker-specified actions using decision feedback, Clean Behavior Anchoring to preserve trigger-free predictive and behavioral fidelity, and Causal Propagation to s...
Wen-Kai Huang, Si-Yuan Liang, Gaolei Li et al.· 0 citations
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