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Jinghe Wang

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#artificial intelligence Preprint Sep 2026

Demonstration-Free Success-Probability Reward Learning for Generalist Robot Policies

This work introduces eVTA, which learns success probabilities from mixed-quality policy rollouts through temporal-difference-style bootstrapping, without expert demonstrations or intermediate annotations, and introduces RL with Evolving Rewards (RLER), a closed-loop framework that adapts eVTA using newly collected roll...

Duo Wu, Hai-Feng Wang, Rongwei Lu et al. · 0 citations
Preprint Aug 2026

GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions

GeniWorld is presented, an interactive world model for robots that generalizes robustly across unseen scenarios by explicitly decoupling embodiment kinematics from environmental dynamics, and generates diverse manipulation trajectories within the world model, improving downstream policy performance and robustness in co...

Cheng-Hao Gu, Hanyang Yu, Jingbo Zhang et al. · 2 citations · ⚡2

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