Abstract
Comment: 8 pages, 5 figures
VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations
- Authors
- Hisham Khalil, Neil Fernandes, Thomas M. Kwok...
- Categories
- cs.RO
Brief
The paper introduces VIDP, an imitation-learning variable-impedance control framework that leverages TP-DAMM to disambiguate geometric variation from intentional compliance in diverse demonstrations and convert trajectory distributions into stiffness profiles. VIDP predicts poses and compliance without force sensing; real-world tests (Khalil et al., 2026) report higher task success and better force/accuracy trade-offs versus fixed-impedance controllers. Full paper metadata provided; summary based on the abstract.
Why it matters
VIDP (Variable Impedance Diffusion Policy) uses a Task-Parameterized Directionality-Aware Mixture Model (TP-DAMM) to extract physically consistent trajectory distributions from diverse demonstrations and maps those distributions to stiffness profiles, enabling joint prediction of pose actions and task compliance without force sensors.
Key details
- Real-world experiments reported in the paper (Khalil et al., 2026) show VIDP significantly outperforms fixed-impedance baselines: it increases task success rate while reducing interaction forces relative to high-stiffness controllers and lowering tracking errors relative to low-stiffness baselines.