Learning Transferable Motor Skills for Geometry-Aware Robotic Surface Tasks

2026-05-24

AI Summary

This work addresses the tight coupling between geometric planning and motion execution in existing robotic surface interaction methods, as well as the limited generalization of imitation-learned policies to novel geometries. To overcome these challenges, the authors propose a modular framework that decouples geometric path planning from expert-level motion execution. The approach dynamically adapts reference trajectories through interpretable atomic motion rules—such as velocity scaling and pose offsetting—and employs a multimodal neural network to jointly learn task trajectories and CAD-based geometric features, thereby inferring rule parameters. This enables, for the first time, a disentangled representation of geometry-aware perception and transferable motor skills. Experiments in dynamic simulation on L-shaped and window-shaped objects successfully extract generalizable motion rules, demonstrating the method’s strong generalization across topologically distinct tasks.

Abstract

Robotic surface-interaction tasks, such as spray painting or welding, require both accurate geometric planning and precise motion execution. While modern motion planners generate valid geometric paths, they often lack the expert motor patterns observed in human operators. Conversely, learning from demonstration often tightly couples task execution to the specific training geometry, limiting transferability. We propose a modular framework that decouples geometric motion planning from execution-level expertise. Expert behavior is represented as a vocabulary of interpretable, atomic motor rules, such as velocity scaling and orientation offsets, that systematically modify a geometrically planned reference path. We train a multimodal neural network to infer rule parameters jointly from kinematic trajectory data and CAD model geometry. We evaluate our approach through dynamic simulation on L-shaped and window-shaped objects, demonstrating on simulated data that the model successfully extracts velocity and orientation rules across both topologies.

Problem

• robotic surface tasks
• motor skills transfer
• geometry-aware planning
• learning from demonstration
• motion execution

Innovation

• modular framework
• motor skill transfer
• geometry-aware robotics
• multimodal learning
• interpretable motor rules