Publikation
Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation
Erik Helmut; Niklas Funk; Tim Schneider; Cristiana de Farias; Jan Peters
In: IEEE International Conference on Robotics and Automation, ICRA 2026, Vienna, Austria, June 1-5, 2026. IEEE International Conference on Robotics and Automation (ICRA), Pages 1815-1822, IEEE, 2026.
Zusammenfassung
Contact-rich manipulation depends on applying
the correct grasp forces throughout the manipulation task,
especially when handling fragile or deformable objects. Most
existing imitation learning approaches often treat visuotactile
feedback only as an additional observation, leaving applied
forces as an uncontrolled consequence of gripper commands.
In this work, we present Force-Aware Robotic Manipulation
(FARM), an imitation learning framework that integrates
high-dimensional tactile data to infer tactile-conditioned force
signals, which in turn define a matching force-based action
space. We collect human demonstrations using a modified ver-
sion of the handheld Universal Manipulation Interface (UMI)
gripper that integrates a GelSight Mini visual tactile sensor.
For deploying the learned policies, we developed an actuated
variant of the UMI gripper with geometry matching our
handheld version. During policy rollouts, the proposed FARM
diffusion policy jointly predicts robot pose, grip width, and
grip force. FARM outperforms several baselines across three
tasks with distinct force requirements—high-force, low-force,
and dynamic force adaptation—demonstrating the advantages
of its two key components: leveraging force-grounded, high-
dimensional tactile observations and a force-based control
space.