Reverse to Advance: Teleoperation-Cost Effective Hard Policy Learning from Reversed Easy Tasks

2026-07-15

AI Summary

This work addresses the challenge that high-cost teleoperated data collection severely limits policy learning for high-precision, difficult robotic tasks. The authors propose a low-cost learning framework that exploits task-direction asymmetry: by automatically resetting the environment and temporally reversing trajectories from easy tasks, they synthesize data for difficult tasks. This approach is integrated with a hierarchical refinement mechanism leveraging kinematic priors and critic-guided advantage filtering, embedded within a closed-loop data collection and online iterative policy learning paradigm. The method substantially reduces reliance on human demonstrations and consistently outperforms existing baselines in both simulation and real-robot experiments, achieving notable improvements in task success rate, data efficiency, and training stability.

Abstract

High-quality teleoperation datasets are costly to collect, particularly for hard tasks. We observe that many tasks exhibit directional asymmetry: completing the forward hard task is difficult, whereas reversing it by relaxing or disrupting the environment is comparatively easy. This suggests that reversed easy-task trajectories can serve as a scalable supervision signal for the hard task, reducing the cost of manual demonstration collection. However, reversed data can be noisy, and directly training on it may yield suboptimal policies. To enable largely automated acquisition and effective use of reversed data, we propose a teleoperation-cost effective framework for hard policy learning via temporal reversal of easy tasks, consisting of three key components: a closed-loop data collection pipeline that alternates between hard-task and easy-task policies to autonomously reset the environment and generate diverse trajectories; a hierarchical data refinement pipeline that temporally inverts easy-task rollouts and filters low-quality motion using kinematic priors and a critic-guided advantage filter; and an iterative policy learning method that trains the hard-task policy using both initial reversed easy-task demonstrations and the filtered reversed data in a continuous online learning loop. By combining automated collection, hierarchical refinement, and iterative learning, our method enables scalable, reliable training of complex, high-precision manipulation tasks. Across two simulated benchmarks and real-robot experiments, we demonstrate that our method improves hard-task success rates with higher data efficiency and more stable training compared to reversal-based and reinforcement-learning baselines, without requiring extensive hard-task teleoperation.

Problem

• teleoperation
• hard task learning
• temporal reversal
• data efficiency
• policy learning

Innovation

• temporal reversal
• teleoperation-cost effective learning
• hierarchical data refinement
• iterative policy learning
• directional asymmetry