Exploration-assisted Bottleneck Transition Toward Robust and Data-efficient Deformable Object Manipulation
Imitation learning has demonstrated impressive results in robotic manipulation but fails under out-of-distribution (OOD) states. This limitation is particularly critical in Deformable Object Manipulation (DOM), where the near-infinite possible configurations render comprehensive data collection infeasible. Although several methods address OOD states, they typically require exhaustive data or highly precise perception. Such requirements are often impractical for DOM owing to its inherent complexities, including self-occlusion. To address the OOD problem in DOM, we propose a novel framework, Exploration-assisted Bottleneck Transition for Deformable Object Manipulation (ExBot), which address...
| Authors | Yujiro Onishi, Ryo Takizawa, Yoshiyuki Ohmura et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.