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Related Experiment Video

Updated: Jul 15, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Learning adaptive reaching and pushing skills using contact information.

Shuaijun Wang¹, Lining Sun¹, Fusheng Zha¹

¹State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.

Frontiers in Neurorobotics

|October 2, 2023

Summary

This study introduces a deep reinforcement learning framework for robot object pushing. The method enhances success rates and generalization to new objects by incorporating contact information into the reward function.

Keywords:

adaptivity contact information pushing reinforcement learning task efficiency

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Robotic manipulation of objects is a challenging task, especially for unseen items.
Developing adaptive control policies for continuous manipulation remains an active research area.

Purpose of the Study:

To propose a deep reinforcement learning (DRL) framework for adaptive robot control in object pushing tasks.
To improve success rates and generalization capabilities for pushing unseen objects from random positions.

Main Methods:

A DRL-based framework was developed for adaptive and continuous robot control.
Contact information was integrated into the reward function design.
A policy was learned through reinforcement learning using a single object in simulation.
The learned policy was tested for generalization to unseen objects.

Main Results:

The proposed approach demonstrated improved success rates and task efficiency compared to methods not using contact information.
The learned policy showed good generalization to unseen objects.
The framework's effectiveness was validated in real-world scenarios.

Conclusions:

Integrating contact information into DRL reward functions enhances robotic object pushing performance.
The developed DRL framework offers a robust solution for adaptive manipulation and generalization to novel objects.