A Shared Autonomy Reconfigurable Control Framework for Telemanipulation of Multi-arm Systems

TL;DR

This paper introduces a flexible telemanipulation control framework for multi-arm robotic systems, allowing intuitive and reconfigurable control modes, including physical interaction tasks, validated through experiments with multiple robots and human subjects.

Contribution

A novel reconfigurable control architecture enabling intuitive, multi-modal teleoperation of multiple robotic arms, including physical interaction capabilities, adaptable to various task configurations.

Findings

Successful control of 4 robots by 8 subjects demonstrating framework versatility.

Framework supports independent and cooperative control modes.

Qualitative results indicate promising applicability for complex tasks.

Abstract

Teleoperation is a widely adopted strategy to control robotic manipulators executing complex tasks that require highly dexterous movements and critical high-level intelligence. Classical teleoperation schemes are based on either joystick control, or on more intuitive interfaces which map directly the user arm motions into one robot arm's motions. These approaches have limits when the execution of a given task requires reconfigurable multiple robotic arm systems. Indeed, the simultaneous teleoperation of two or more robot arms could extend the workspace of the manipulation cell, or increase its total payload, or afford other advantages. In different phases of a reconfigurable multi-arm system, each robot could act as an independent arm, or as one of a pair of cooperating arms, or as one of the fingers of a virtual, large robot hand. This manuscript proposes a novel telemanipulation framework that enables both the individual and combined control of any number of robotic arms. Thanks to the designed control architecture, the human operator can intuitively choose the proposed control modalities and the manipulators that make the task convenient to execute through the user interface. Moreover, through the tele-impedance paradigm, the system can address complex tasks that require physical interaction by letting the robot mimic the arm impedance and position references of the human operator. The proposed framework is validated with 8 subjects controlling 4 Franka Emika Panda robots with 7-DoFs to execute a telemanipulation task. Qualitative results of the experiments show us the promising applicability of our framework.