Learning Force-Regulated Manipulation with a Low-Cost Tactile-Force-Controlled Gripper

TL;DR

This paper introduces a low-cost tactile-force-controlled gripper and a reactive force regulation framework, RETAF, enabling robots to manipulate objects with precise force control, improving stability and performance in real-world tasks.

Contribution

The work presents TF-Gripper, a low-cost tactile-force sensor-integrated gripper, and RETAF, a framework for reactive force regulation, advancing force-controlled manipulation in robotics.

Findings

Force control improves grasp stability and task success.

Tactile feedback is crucial for effective force regulation.

RETAF outperforms baseline methods in real-world tasks.

Abstract

Successfully manipulating many everyday objects, such as potato chips, requires precise force regulation. Failure to modulate force can lead to task failure or irreversible damage to the objects. Humans can precisely achieve this by adapting force from tactile feedback, even within a short period of physical contact. We aim to give robots this capability. However, commercial grippers exhibit high cost or high minimum force, making them unsuitable for studying force-controlled policy learning with everyday force-sensitive objects. We introduce TF-Gripper, a low-cost (~$150) force-controlled parallel-jaw gripper that integrates tactile sensing as feedback. It has an effective force range of 0.45-45N and is compatible with different robot arms. Additionally, we designed a teleoperation device paired with TF-Gripper to record human-applied grasping forces. While standard low-frequency policies can be trained on this data, they struggle with the reactive, contact-dependent nature of force regulation. To overcome this, we propose RETAF (REactive Tactile Adaptation of Force), a framework that decouples grasping force control from arm pose prediction. RETAF regulates force at high frequency using wrist images and tactile feedback, while a base policy predicts end-effector pose and gripper open/close action. We evaluate TF-Gripper and RETAF across five real-world tasks requiring precise force regulation. Results show that compared to position control, direct force control significantly improves grasp stability and task performance. We further show that tactile feedback is essential for force regulation, and that RETAF consistently outperforms baselines and can be integrated with various base policies. We hope this work opens a path for scaling the learning of force-controlled policies in robotic manipulation. Project page: https://force-gripper.github.io .