A Data-Driven Approach for Contact Detection, Classification and Reaction in Physical Human-Robot Collaboration

TL;DR

This paper presents a two-stage RNN-based method for detecting and classifying human-robot contact types in shared workspaces, enabling appropriate robot reactions to ensure safe and effective collaboration.

Contribution

It introduces a novel two-stage RNN approach for contact detection and classification in human-robot interaction, incorporating control barrier functions for safety.

Findings

Effective detection of contact occurrence and type in real-time

Successful validation on a Kinova Jaco2 robot setup

Enhanced safety and collaboration efficiency in shared workspaces

Abstract

This paper considers a scenario where a robot and a human operator share the same workspace, and the robot is able to both carry out autonomous tasks and physically interact with the human in order to achieve common goals. In this context, both intentional and accidental contacts between human and robot might occur due to the complexity of tasks and environment, to the uncertainty of human behavior, and to the typical lack of awareness of each other actions. Here, a two stage strategy based on Recurrent Neural Networks (RNNs) is designed to detect intentional and accidental contacts: the occurrence of a contact with the human is detected at the first stage, while the classification between intentional and accidental is performed at the second stage. An admittance control strategy or an evasive action is then performed by the robot, respectively. The approach also works in the case the robot simultaneously interacts with the human and the environment, where the interaction wrench of the latter is modeled via Gaussian Mixture Models (GMMs). Control Barrier Functions (CBFs) are included, at the control level, to guarantee the satisfaction of robot and task constraints while performing the proper interaction strategy. The approach has been validated on a real setup composed of a Kinova Jaco2 robot.