A curated, searchable collection of robotics research with open-source implementations—so you can read, run, and reproduce.
Recent work selected for practical relevance and research impact.
Satoshi Yamamori, Koji Ishihara, Kentaro Minamikawa et al.
Instead of making simulation imitate every motor imperfection, this work shapes the physical actuator to follow the simulated reference.
Hexian Ni, Tao Lu, Yinghao Cai
CoRe combines code-based rewards with learned visual preferences so that neither reward source has to capture the entire task alone.
Chenyang Ma, Yue Yang, Radu Corcodel et al.
FurnitureVLA makes assembly progress an explicit prediction, helping a bimanual policy decide when to move to the next subtask.
Chi Zhang, Penglin Cai, Ziheng Xi et al.
TTP transfers tactile experience from human demonstrations into robot policies for contact-sensitive manipulation.
William English, Hao Zheng, Rickard Ewetz
This method checks a predicted action sequence for future collisions while the VLA is still generating it, rather than correcting only the next action.
Yuwan Liu, Hongze Yu, Song Liu et al.
Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification.
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