Problem
Research questions and friction points this paper is trying to address.
Soft robot motion planning
Suction-based locomotion
Hybrid contact-motion planning
3D surface navigation
FlexWorm: Primitive-augmented Hybrid Contact-motion Planning for Suction-based Multi-segment Deformable Robots
🤖 AI Summary
This study addresses the reliance on manual gaits and lack of generalizable planning in multi-segmented suction-cup soft robots navigating complex surfaces. We propose a hybrid contact motion planning framework that integrates partitioned inverse kinematics search with primitive-augmented retrieval. By learning observation-primitive embeddings, this approach accelerates proposal generation, supports failure recovery, and enables coordinated planning for discrete adhesion and continuous deformation. Both simulation and hardware experiments demonstrate that the proposed framework significantly reduces planning time while maintaining high success rates. These results effectively validate its executability and robustness in uncertain environments, offering a viable solution for autonomous navigation of soft robots on unstructured terrain without requiring predefined gait sequences.
📝 Abstract
Multi-segment suction-based soft robots are promising for inspection and maintenance in confined or fragile environments, but existing approaches still depend heavily on manually designed gaits and environment-specific motion scripts. This work presents a planning framework for serial multi-segment soft robots with deformable body segments and boundary suction pads. The formulation targets full 3D navigation on complex surfaces and explicitly handles discrete adhesion switching and continuous body deformation under geometric, collision, and quasi-static feasibility constraints, while remaining agnostic to the specific actuation realization used to produce segment deformation. Its core, block-wise IK hybrid search (IKHS), performs best-first search over feasible adhesion transitions while solving inverse kinematics only on induced free blocks. On top of IKHS, primitive-augmented hybrid search (PaHS) uses a learned observation--primitive embedding to retrieve short validated motion segments for fast local proposal, with fallback to standard IKHS branching when retrieval fails. In simulation, the framework consistently outperforms controlled baselines in planning success, transition quality, and efficiency across diverse terrains. PaHS matches IKHS in success rate while substantially reducing planning time. Repeated hardware experiments on a pneumatic multi-segment soft robot further demonstrate executability and online recovery under actuation and adhesion uncertainty.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid Contact-motion Planning
Primitive-augmented Hybrid Search
Block-wise Inverse Kinematics
Multi-segment Soft Robot
Suction-based Locomotion
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