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<p>High-quality robot learning data is essential for advancing Vision-Language-Action (VLA) models, yet collecting it at scale remains the primary bottleneck in their development. This bottleneck is further amplified for manipulation tasks that require tight pose alignment or impose geometric constraints, where small errors lead to task failure, such as peg-in-hole. To address this, we propose Reverse Playback Motion (RPM), a novel data collection framework for robot manipulation. Rather than attempting to precisely place an object into a target configuration, operators instead start from the goal state and perform the reverse motion, which is inherently easier to execute. The collected reverse motions are then reconstructed into valid forward trajectories through time-reversal of the recorded end-effector poses. We evaluate RPM across five constrained manipulation tasks, including both single-stage and multi-stage scenarios, and demonstrate that it enables up to 3 times faster data collection for non-expert operators, while achieving superior policy performance compared to conventionally collected demonstrations.</p>

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Reverse Playback Motion: A Simple Way for Anyone to Obtain High-Quality Demonstration Data

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Hyunjun Kim,
  • Jaeseog Won,
  • Hong-ryul Jung,
  • Donghoon Kim,
  • Minsuk Ko,
  • Hyungpil Moon

摘要

High-quality robot learning data is essential for advancing Vision-Language-Action (VLA) models, yet collecting it at scale remains the primary bottleneck in their development. This bottleneck is further amplified for manipulation tasks that require tight pose alignment or impose geometric constraints, where small errors lead to task failure, such as peg-in-hole. To address this, we propose Reverse Playback Motion (RPM), a novel data collection framework for robot manipulation. Rather than attempting to precisely place an object into a target configuration, operators instead start from the goal state and perform the reverse motion, which is inherently easier to execute. The collected reverse motions are then reconstructed into valid forward trajectories through time-reversal of the recorded end-effector poses. We evaluate RPM across five constrained manipulation tasks, including both single-stage and multi-stage scenarios, and demonstrate that it enables up to 3 times faster data collection for non-expert operators, while achieving superior policy performance compared to conventionally collected demonstrations.