Author

Puze Liu

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Preprint Sep 2026

Find Something You Can't Do: Agentic Real-World Reinforcement Learning for Self-Improving VLA Models

FIND is introduced, an agentic real-world RL framework that closes the loop between scene understanding, weakness-aware practice, self-evaluation, and policy improvement in a persistent workspace and reframes autonomous practice as a scene-conditioned, performance-aware task-selection problem.

Yuan Fang, Ze-Chu Li, Hao-Lei Tong et al. · 0 citations

Preprint Aug 2026

Blind Dexterity: Whole-Body Humanoid Manipulation via Pure Proprioception

Joint encoder-based proprioception, combined with compliant actuation (now widely available on commercial robots and low-cost motors) is already a strong, practical substrate for whole-body dexterous manipulation and interactive perception, and therefore a natural foundation on which richer sensing can be layered.

Aditya Bhatt, Oleg Kaidanov, Pu-Ze Liu et al. · 1 citation

Jul 2026

Directional Constraints for Efficient Exploration in Safe Reinforcement Learning

This work proposes an extension of the ATACOM framework, a state-of-the-art reliable safety layer that can be integrated with existing Reinforcement Learning algorithms to enforce constraints derived from prior knowledge of the system or learned directly from data.

Paolo Magliano, Puze Liu, Jan Peters et al. · arXiv.org · 0 citations