DG-ACMP: Deformation-Guided Motion Planning With Acceptable Contacts for Manipulators in Cluttered Environments

Abstract

In cluttered environments where rigid and deformable objects coexist, collision-free paths often do not exist. Planners that enforce collision-free trajectories therefore perform poorly by excluding feasible contact-aware trajectories. We introduce the deformation-guided acceptable-contact motion planning (DG-ACMP) framework, which enables controllable contact with deformable objects while avoiding rigid objects to enhance dexterity and feasibility. DG-ACMP employs a Kelvin–Voigt viscoelastic model for soft contact, integrated with obstacle-specific Laplacian deformation fields that approximate normal strain and mitigate local minima issues in thin-plate obstacles, in contrast to traditional SDF-based approaches. The contact model is formulated as likelihood factors within a Gaussian Process Motion Planning (GPMP) factor graph, enabling efficient trajectory optimization. Compared to contact-implicit optimization baselines, DG-ACMP reduces optimization time by up to two orders of magnitude while achieving higher success rates and lower contact forces in comparative simulations across non-convex, narrow-passage, and cluttered scenes. Real-world experiments on a Franka Emika manipulator in cluttered cabinet tasks demonstrate DG-ACMP's ability to produce gentle, velocity-regulated contacts with compliant items, succeeding where collision-free paths are infeasible.