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

2026-02-10

AI Summary

Existing robotic grasping systems struggle to precisely modulate contact forces on force-sensitive objects—such as potato chips—often resulting in damage or task failure. To address this challenge, this work proposes TF-Gripper, a low-cost tactile force-controlled gripper with a wide force range (0.45–45 N), and introduces the RETAF framework, which enables reactive force modulation by fusing high-frequency tactile feedback with visual information. RETAF decouples gripper force control from the manipulator’s pose policy, allowing seamless integration of diverse base strategies. Evaluated across five real-world grasping tasks, the approach significantly outperforms conventional position-based control, demonstrating both the critical role of tactile feedback in fine-grained force regulation and the strong generalization capability of the RETAF framework.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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 .

Problem

• force regulation
• tactile feedback
• robotic manipulation
• force-sensitive objects
• grasping

Innovation

• tactile-force control
• low-cost gripper
• reactive force regulation
• teleoperation data collection
• policy decoupling