Learning High-Difficulty Manipulation Strategies from Time-Reversed Data of Easy Tasks
What happened
Qiyuan Qiao and other authors propose a teleoperation cost-effective framework that uses time-reversed trajectories of easy tasks as supervision signals for high-difficulty manipulation tasks, thereby reducing manual demonstration collection. The framework consists of three parts: a closed-loop data collection pipeline, hierarchical data refinement based on kinematic priors and critic-guided advantage filtering, and iterative policy learning. The authors claim that in two simulation benchmarks and real-robot experiments, this method improves success rates on high-difficulty tasks compared with reversal-based and reinforcement-learning baselines, with higher data efficiency, more stable training, and without the need for large amounts of teleoperation data for high-difficulty tasks.
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- arXiv cs.ROReverse 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.
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