AI RESEARCH
Exploration-assisted Bottleneck Transition Toward Robust and Data-efficient Deformable Object Manipulation
arXiv CS.CV
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ArXi:2603.13756v1 Announce Type: cross Imitation learning has nstrated 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.