Reverse to Advance: Learning Hard Manipulation Policies with Reversed Easy-Task Data to Cut Teleoperation Costs

This paper proposes a teleoperation-cost-effective framework that uses temporally reversed trajectories from easy tasks as supervision signals for hard manipulation tasks, reducing manual demonstration collection. The framework has three parts: a closed-loop data collection pipeline, hierarchical data refinement based on kinematic priors and critic-guided advantage filtering, and iterative policy learning. In two simulation benchmarks and real-robot experiments, the method improved success rates on hard tasks compared with reversal-based and reinforcement-learning baselines, with higher data efficiency and more stable training.