Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks

• URL: http://arxiv.org/abs/2509.19696v1
• Date: Wed, 24 Sep 2025 02:07:17 GMT
• Title: Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks
• Authors: Noah Geiger, Tamim Asfour, Neville Hogan, Johannes Lachner,
• Abstract summary: Impedance Control shapes physical interaction but requires task-aware tuning by selecting feasible impedance parameters.<n>We present Diffusion-Based Impedance Learning, a framework that combines both domains.
• Score: 13.17252299377244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning methods excel at motion generation in the information domain but are not primarily designed for physical interaction in the energy domain. Impedance Control shapes physical interaction but requires task-aware tuning by selecting feasible impedance parameters. We present Diffusion-Based Impedance Learning, a framework that combines both domains. A Transformer-based Diffusion Model with cross-attention to external wrenches reconstructs a simulated Zero-Force Trajectory (sZFT). This captures both translational and rotational task-space behavior. For rotations, we introduce a novel SLERP-based quaternion noise scheduler that ensures geometric consistency. The reconstructed sZFT is then passed to an energy-based estimator that updates stiffness and damping parameters. A directional rule is applied that reduces impedance along non task axes while preserving rigidity along task directions. Training data were collected for a parkour scenario and robotic-assisted therapy tasks using teleoperation with Apple Vision Pro. With only tens of thousands of samples, the model achieved sub-millimeter positional accuracy and sub-degree rotational accuracy. Its compact model size enabled real-time torque control and autonomous stiffness adaptation on a KUKA LBR iiwa robot. The controller achieved smooth parkour traversal within force and velocity limits and 30/30 success rates for cylindrical, square, and star peg insertions without any peg-specific demonstrations in the training data set. All code for the Transformer-based Diffusion Model, the robot controller, and the Apple Vision Pro telemanipulation framework is publicly available. These results mark an important step towards Physical AI, fusing model-based control for physical interaction with learning-based methods for trajectory generation.

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