Forces for free: Vision-based contact force estimation with a compliant hand

Abstract

Force-sensing capabilities are essential for robot manipulation systems. However, commonly used wrist-mounted force/torque sensors are heavy, fragile, and expensive, and tactile sensors require adding fragile circuitry to the robot fingers while only providing force information local to the contact. Here, we present a vision-based contact force estimator that serves as a more cost-effective and easier-to-implement alternative to existing force sensors by leveraging deformations of compliant hands upon contacts when compliant hands are in use. Our approach uses an estimator that visually observes a specialized compliant robot hand (available open source with easy fabrication through 3D printing) and predicts the contact force on the basis of its elastic deformation upon external forces. Because using wrist-mounted cameras to observe the gripper is common for robot manipulation systems, our method can obtain additional force information provided that the gripper is compliant. We optimized our compliant hand to minimize friction and avoid singularities in finger configurations, and we introduced memory to the estimator to combat the partial observability of the contact forces from the remaining friction and hysteresis. In addition, the estimator was made robust to background distractions and finger occlusions using vision foundation models to segment out the fingers. Although it is less accurate and slower than commercial force/torque sensors, we experimentally demonstrated the accuracy and robustness of our estimator (achieving between 0.2 newton and 0.4 newton error) and its utility during a variety of manipulation tasks using the gripper in the presence of noisy backgrounds and occlusions.