Exploration-assisted Bottleneck Transition Toward Robust and Data-efficient Deformable Object Manipulation

TL;DR

This paper introduces ExBot, a novel framework for deformable object manipulation that enhances robustness and data efficiency by using standardized bottleneck states and exploration strategies to handle out-of-distribution scenarios.

Contribution

ExBot proposes a new approach that reduces demonstration needs and improves robustness in deformable object manipulation by standardizing initial configurations and partitioning state space based on recognizability.

Findings

Successful real-world manipulation of ropes and cloth from diverse OOD states.

Achieved robustness to severe self-occlusions and perception errors.

Reduced demonstration requirements through bottleneck states.

Abstract

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 addresses the OOD challenge through two key advantages. First, we introduce bottleneck states, standardized configurations that serve as starting points for task execution. This enables the reconceptualization of OOD challenges as the problem of transitioning diverse initial states to these bottleneck states, significantly reducing demonstration requirements. Second, to account for imperfect perception, we partition the OOD state space based on recognizability and employ dual action primitives. This approach enables ExBot to manipulate even unrecognizable states without requiring accurate perception. By concentrating demonstrations around bottleneck states and leveraging exploration to alter perceptual conditions, ExBot achieves both data efficiency and robustness to severe OOD scenarios. Real-world experiments on rope and cloth manipulation demonstrate successful task completion from diverse OOD states, including severe self-occlusions.