Hai-Feng Wang

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...