Skip to content
aiqbench takeAI Research

aiqbench analysis

This research advances Virtual Model Control (VMC) by adapting it from legged locomotion to force-controlled robotic arms for compliant reaching tasks under uncertainty. The study validates six virtual model designs on an 8-DOF humanoid robot, demonstrating that while some accuracy is sacrificed, the method significantly improves adaptability to obstacles and external forces. This work represents a meaningful step in making robust control strategies applicable to diverse robotic systems beyond their original domain.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Virtual model control for compliant reaching under uncertainties

arXiv2 min read

arXiv:2610.05695v1 Announce Type: new Abstract: Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was primarily investigated for legged robot locomotion in the past, it can be more generally applicable to other types of robotic systems. This paper investigates the VMC framework for reaching tasks in a force-controlled robotic arm. We propose six different approaches to designing virtual models in order to achieve reaching tasks in environments with obstacles and uncertainties. A force-controlled 8 degree-of-freedom humanoid robot was used to validate the proposed approach in the real world. We conducted three experiments to test the performance of VMC controllers in terms of predictability, sensitivity to external force, and adaptability against known and unknown obstacles. Experimental analyses show that, even though the proposed approach needs to sacrifice accuracy and trajectory optimality, it enables us to design complex reaching motions under uncertainties, in an intuitive and extendable manner.

Abstract:Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was primarily investigated for legged robot locomotion in the past, it can be more generally applicable to other types of robotic systems. This paper investigates the VMC framework for reaching tasks in a force-controlled robotic arm. We propose six different approaches to designing virtual models in order to achieve reaching tasks in environments with obstacles and uncertainties. A force-controlled 8 degree-of-freedom humanoid robot was used to validate the proposed approach in the real world. We conducted three experiments to test the performance of VMC controllers in terms of predictability, sensitivity to external force, and adaptability against known and unknown obstacles. Experimental analyses show that, even though the proposed approach needs to sacrifice accuracy and trajectory optimality, it enables us to design complex reaching motions under uncertainties, in an intuitive and extendable manner.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2610.05695 [cs.RO]

(or arXiv:2610.05695v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.05695 arXiv-issued DOI via DataCite (pending registration)

Y. Zhang, D. Larby, F. Iida and F. Forni, "Virtual model control for compliant reaching under uncertainties," Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 2024, pp. 795-801, doi: 10.1109/IROS58592.2024.10801592

Full text under the publisher's open licence · 204 words

Related coverage