Multifingered force-aware control for humanoid robots

2026-03-09

AI Summary

This work addresses the challenge of force perception and distribution in multi-fingered humanoid robot hands when grasping objects with uneven mass distribution or unstable contacts. The authors propose a general control framework based on estimated contact forces, which leverages data from Xela magnetic tactile sensors to train a force estimation model. Rather than using raw tactile signals, the framework directly employs the estimated forces as input to coordinately regulate the motion of the torso, arms, wrists, and fingers, driving the center of pressure at the fingertips toward the centroid of the contact polygon to achieve stable grasps. The approach is compatible with any sensor capable of force estimation and enables dynamic force redistribution among fingers. Experimental results demonstrate an 82.7% success rate across five object-balancing tasks and 80% accuracy in multi-object scenarios.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we address force-aware control and force distribution in robotic platforms with multi-fingered hands. Given a target goal and force estimates from tactile sensors, we design a controller that adapts the motion of the torso, arm, wrist, and fingers, redistributing forces to maintain stable contact with objects of varying mass distribution or unstable contacts. To estimate forces, we collect a dataset of tactile signals and ground-truth force measurements using five Xela magnetic sensors interacting with indenters, and train force estimators. We then introduce a model-based control scheme that minimizes the distance between the Center of Pressure (CoP) and the centroid of the fingertips contact polygon. Since our method relies on estimated forces rather than raw tactile signals, it has the potential to be applied to any sensor capable of force estimation. We validate our framework on a balancing task with five objects, achieving a $82.7\%$ success rate, and further evaluate it in multi-object scenarios, achieving $80\%$ accuracy. Code and data can be found here https://github.com/hsp-iit/multifingered-force-aware-control.

Problem

• force-aware control
• force distribution
• multi-fingered hands
• tactile sensing
• stable contact

Innovation

• force-aware control
• multifingered manipulation
• tactile sensing
• center of pressure
• force estimation