[2509.19696] Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks

Computer Science > Robotics arXiv:2509.19696 (cs) [Submitted on 24 Sep 2025 (v1), last revised 5 Mar 2026 (this version, v3)] Title:Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks Authors:Noah Geiger, Tamim Asfour, Neville Hogan, Johannes Lachner View a PDF of the paper titled Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks, by Noah Geiger and 2 other authors View PDF HTML (experimental) Abstract:Learning-based methods excel at robot motion generation but remain limited in contact-rich physical interaction. Impedance control provides stable and safe contact behavior but requires task-specific tuning of stiffness and damping parameters. We present Diffusion-Based Impedance Learning, a framework that bridges these paradigms by combining generative modeling with energy-consistent impedance control. A Transformer-based Diffusion Model, conditioned via cross-attention on measured external wrenches, reconstructs simulated Zero-Force Trajectories (sZFTs) that represent contact-consistent equilibrium behavior. A SLERP-based quaternion noise scheduler preserves geometric consistency for rotations on the unit sphere. The reconstructed sZFT is used by an energy-based estimator to adapt impedance online through directional stiffness and damping modulation. Trained on parkour and robot-assisted therapy demonstrations collected via Apple Vision Pro teleoperation, the model achieves sub-millimeter positional and sub-degree rotational accuracy...