Learning Constraint-Aware Dynamical Systems from Human Demonstrations for Constrained Manipulation Tasks

Abstract

Learning from demonstration (LfD) enables robots to acquire new skills from human examples without explicit programming. Dynamical system (DS)-based approaches, in particular, have shown robustness to disturbances and adaptability in unstructured environments. However, existing methods often fail to incorporate task-specific constraints—such as grasp locations, execution starting points, or motion restrictions—that are critical for reliable execution. This limitation becomes especially problematic in tool-use scenarios, where both the environment and the grasped tool impose strict restrictions on feasible motions. To address this challenge, we propose a novel constraint-aware DS framework that automatically extracts and encodes task-specific constraints directly from demonstration data. The key idea is that task-critical configurations, repeatedly observed across successful demonstrations, can be identified and modeled as essential regions for task success using Gaussian Process Regression. By embedding these constraints, the proposed method generates motions that remain robust to environmental variations and tool-induced limitations. Experiments with a 7-DoF robotic manipulator demonstrate that our framework significantly improves task success rates over state-of-the-art methods. Real-world evaluations on daily-life tasks, such as dishware collection, further confirm its practicality and potential for real-world robotic applications.