Foresight Residual RL Improves Long-Horizon Robot Manipulation with VLA Policies
A new method called Foresight Residual RL is proposed to improve long-horizon robot manipulation tasks by optimizing the quality of handoffs between subtasks. The approach augments sparse success rewards with an offline-estimated foresight value that predicts the likelihood of future subtask success, leading to a significant increase in full-task success rates on a challenging nut-tightening assembly benchmark (85.6%), compared to standard residual RL (54.5%) and VLA baselines.
Why it matters: This work demonstrates that optimizing terminal state quality across subtasks is crucial for improving the performance of chained vision-language-action policies in complex, contact-rich robotic assembly tasks.
Full story at: arXiv Robotics ↗