aiqbench analysis
The paper tackles a practical bottleneck in robot learning: training manipulation policies from ordinary human videos instead of costly teleoperation data. Its main idea is to use object trajectories, extracted with off-the-shelf 6D pose estimation, as an embodiment-agnostic intermediate representation for generating training data. The reported near-100% success on seven RLBench tasks is simulation-only, so real-world cross-embodiment claims from one-shot demonstrations are the part to watch for independent replication.
Trajectory-based Diffusion from One-shot Human Video for Generalizable Manipulation
arXiv:2610.21792v1 Announce Type: new Abstract: Imitation learning enables robots to perform complex tasks by leveraging human-collected demonstrations. However, most existing approaches require labor-intensive data collection with additional teleoperation systems, which hinders scalability. Human videos represent a natural source of data that implicitly contain knowledge of manipulation behaviors. However, transferring them to robots remains challenging due to the visual domain gap and the absence of explicit action labels. In this work, we introduce TDV, a trajectory-based framework that leverages data generation to enable generalizable manipulation. TDV focuses on task-relevant objects by leveraging plug-and-play 6D pose estimation techniques to extract object trajectories from human videos, thereby mitigating the impact of irrelevant backgrounds and varying viewpoints. As a modality-agnostic intermediate representation, trajectories are inherently independent of the visual domains and different embodiments, enabling efficient data generation. Our method outperforms previous approaches on RLBench simulation tasks, achieving near 100\% success rates across seven tasks. In real-world experiments, TDV enables cross-embodiment generalization manipulation from one-shot human demonstrations by leveraging efficient trajectory generation techniques.
Abstract:Imitation learning enables robots to perform complex tasks by leveraging human-collected demonstrations. However, most existing approaches require labor-intensive data collection with additional teleoperation systems, which hinders scalability. Human videos represent a natural source of data that implicitly contain knowledge of manipulation behaviors. However, transferring them to robots remains challenging due to the visual domain gap and the absence of explicit action labels. In this work, we introduce TDV, a trajectory-based framework that leverages data generation to enable generalizable manipulation. TDV focuses on task-relevant objects by leveraging plug-and-play 6D pose estimation techniques to extract object trajectories from human videos, thereby mitigating the impact of irrelevant backgrounds and varying viewpoints. As a modality-agnostic intermediate representation, trajectories are inherently independent of the visual domains and different embodiments, enabling efficient data generation. Our method outperforms previous approaches on RLBench simulation tasks, achieving near 100\% success rates across seven tasks. In real-world experiments, TDV enables cross-embodiment generalization manipulation from one-shot human demonstrations by leveraging efficient trajectory generation techniques.
Robotics (cs.RO)
arXiv:2610.21792 [cs.RO]
(or arXiv:2610.21792v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.21792 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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