Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks
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.
Teleoperated Data Collection for Model Training
1. Human-Guided Upper Limb Therapy Demonstration via Apple Vision Pro
Teleoperation is used to record data of patient movements with varying levels of support, enabling the model to learn the dynamic relationship between force and motion.
More about the work regarding Physics-Informed Diffusion for Physical Therapist-Like Robot Assistance can be found here: https://strokeairobotics.github.io/StrokeAI
2. Parkour Trajectory Demonstration via Teleoperation
Teleoperation is used to guide the robot through physical obstacles, enabling the model to learn how interaction forces vary with pose and context.
Model Deployment in Real-World Contact Tasks
The robot autonomously assists with upper-limb rehabilitation by adjusting support in real time based on patient engagement.
These videos compare the robot using constant stiffness parameters (left) versus diffusion-based adaptive impedance (right). With constant stiffness, the robot fails to complete the task due to speed and force limitations. With adaptive impedance, the robot successfully completes the task, smoothly traversing all obstacles.
The robot performs peg insertion tasks with cylindrical, square, and star-shaped pegs using constant stiffness parameters. The cylindrical peg was inserted successfully in 30/30 trials, the square peg in 4/30 trials, and the star peg in none of the 30 trials. These trials highlight the difficulty of choosing feasible fixed impedance in contact-rich environments.
Diffusion-Based Impedance Learning achieved a 30/30 success rate for all three peg types. These results highlight that with increasing insertion complexity, more advanced strategies such as our diffusion-based approach become essential. The outcome is particularly notable because the training data included only parkour and upper-limb rehabilitation data and no peg-in-hole demonstrations.
Related Publications
- N. Geiger, T. Asfour, N. Hogan, and J. Lachner, “Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks,” IEEE Transactions on Robotics, submitted, 2025. [arXiv] [Github repo]
- N. Geiger, C. Piazza, T. Asfour, N. Hogan, and J. Lachner, “Physics-Grounded Diffusion for Physical Therapist-Like Robot Assistance,” in preparation, 2025.