FoldPath: End-to-End Object-Centric Motion Generation via Modulated Implicit Paths

TL;DR

FoldPath is an innovative end-to-end neural field method for object-centric motion generation that produces smooth, continuous robot paths, reducing reliance on post-processing and demonstrating strong performance even with limited expert data.

Contribution

It introduces a novel neural field approach for continuous motion prediction in object-centric tasks, eliminating the need for post-processing of discrete waypoints.

Findings

Outperforms recent learning-based methods in predictive accuracy.

Generalizes effectively in real industrial scenarios with limited data.

Provides comprehensive metrics for evaluating long-horizon robotic paths.

Abstract

Object-Centric Motion Generation (OCMG) is instrumental in advancing automated manufacturing processes, particularly in domains requiring high-precision expert robotic motions, such as spray painting and welding. To realize effective automation, robust algorithms are essential for generating extended, object-aware trajectories across intricate 3D geometries. However, contemporary OCMG techniques are either based on ad-hoc heuristics or employ learning-based pipelines that are still reliant on sensitive post-processing steps to generate executable paths. We introduce FoldPath, a novel, end-to-end, neural field based method for OCMG. Unlike prior deep learning approaches that predict discrete sequences of end-effector waypoints, FoldPath learns the robot motion as a continuous function, thus implicitly encoding smooth output paths. This paradigm shift eliminates the need for brittle post-processing steps that concatenate and order the predicted discrete waypoints. Particularly, our approach demonstrates superior predictive performance compared to recently proposed learning-based methods, and attains generalization capabilities even in real industrial settings, where only a limited amount of 70 expert samples are provided. We validate FoldPath through comprehensive experiments in a realistic simulation environment and introduce new, rigorous metrics designed to comprehensively evaluate long-horizon robotic paths, thus advancing the OCMG task towards practical maturity.