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

TL;DR

To enable force-regulated manipulation of everyday objects, the authors design TF-Gripper, a low-cost tactile-force-controlled gripper, and RETAF, a high-frequency force adaptation policy that decouples force control from end-effector pose prediction. They collect force-ground-truth demonstrations via a teleoperation device and evaluate on five real-world tasks, showing force control improves grasp stability and task success over position control. Tactile feedback is crucial for robust force regulation, and RETAF consistently outperforms diffusion-based baselines and can integrate with different base policies. The work demonstrates a practical path toward scalable learning of force-controlled manipulation using inexpensive hardware.

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 .

Figures (11)

• Figure 1: To mimic the human ability to regulate force through tactile feedback to manipulate everyday objects, we develop a force-controlled gripper with tactile sensing for learning reactive and gentle manipulation policies.
• Figure 2: Gripper position vs. force control on objects with similar appearance but different physical properties. (a) The same type of potato chips but with a small variation in size. (b) The same cherry tomato after two days of softening.
• Figure 3: Hardware design of the TF-Gripper. (a) Key components: An adapter connects the gripper to a robot and two motors actuate the fingertips along linear rails by pulling a timing belt. (b) The overall TF-Gripper setup, with the main structure fully 3D printed. (c) A camera provides a wrist view, with soft fingertips integrated with tactile sensors.
• Figure 4: Actuator current (PWM)--force relationship. The force is measured at the fingertip contact surface, reflecting the effective grasping force applied by the TF-Gripper.
• Figure 5: Design of the teleoperation device. A VR controller controls the robot's end-effector pose. Two figure rings are connected to the motors; they detect the force applied by the human operator to drive the TF-Gripper.