cs.ROFeb 16, 2026

Augmenting Human Balance with Generic Supernumerary Robotic Limbs

Authors: Xuanyun Qiu, Dorian Verdel, Hector Cervantes-Culebro, Alexis Devillard, Etienne Burdet

Organizations: All authors are with the Bioengineering Department, Imperial College of Science, Technology and Medicine, W12 0BZ London, United-Kingdom

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Supernumerary robotic limbs (SLs) have the potential to transform a wide range of human activities, yet their usability remains limited by key technical challenges, particularly in ensuring safety and achieving versatile control. Here, we address the critical problem of maintaining balance in the human-SLs system, a prerequisite for safe and comfortable augmentation tasks. Unlike previous approaches that developed SLs specifically for stability support, we propose a general framework for preserving balance with SLs designed for generic use. Our hierarchical three-layer architecture consists of: (i) a prediction layer that estimates human trunk and center of mass (CoM) dynamics, (ii) a planning layer that generates optimal CoM trajectories to counteract trunk movements and computes the corresponding SL control inputs, and (iii) a control layer that executes these inputs on the SL hardware. We evaluated the framework with ten participants performing forward and lateral bending tasks. The results show a clear reduction in stance instability, demonstrating the framework's effectiveness in enhancing balance. This work paves the path towards safe and versatile human-SLs interactions. [This paper has been submitted for publication to IEEE.]

Figures & tables

Fig. 1: Hierarchical control structure for human-SLs balance. The top layer estimates the human-SLs system state and predicts future states using LQE. The middle layer plans (i) a trajectory that minimizes the shift of the human-SLs center of mass projected in a horizontal plane, and (ii) the future torques required to follow this trajectory. The bottom layer generates motor commands for the SLs.
Fig. 2: Experimental setup. A. MUVE system integrating motion capture, an orientable split treadmill, up to four SLs and two cobots. B. Definition of the planes used to control the SLs and positions of the motion capture markers used to extract these planes. C. Nomenclature of one SL’s segments and joints. D. Protocol used to systematically induce instability in the human-SLs system. E,F. Photos of a representative participant with SLs providing active stabilization during a frontal bow trial (E) and a lateral bow trial (F).
Fig. 3: Results in frontal bow trials (top) and lateral bow trials (bottom). A-C. Evolution of the CoM-SUP distance for each individual (thin lines) and the population average (thick line), in the HOnly, NoComp and Comp conditions respectively. D. Average CoM-SUP distance across the three conditions for all ten participants. E. Ground reaction force (GRF) in the three conditions. Ellipses indicate the maximum GRF of the population average, and the dots show the force distribution for a representative participant.
Fig. 4: CoP-SUP distance. Average distance across each trial for every participant in A frontal bow trials and B lateral bow trials.

Explore similar work

CardsList

  1. Reconfiguration of supernumerary robotic limbs for human augmentation

    Mar 31, 2026Mustafa Mete, Anastasia Bolotnikova, Alexander Schuessler +1RoboticsAssistive Robotics

    Reconfigurable Robotics Lab, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. · Laboratory for Analysis and Architecture of Systems (LAAS), CNRS, Toulouse, France.

  2. SteadyTray: Learning Object Balancing Tasks in Humanoid Tray Transport via Residual Reinforcement Learning

    Mar 11, 2026Anlun Huang, Zhenyu Wu, Soofiyan Atar +2Robotic ControlResidual Policy Learning

    UC San Diego

  3. Embedding Classical Balance Control Principles in Reinforcement Learning for Humanoid Recovery

    Mar 9, 2026Nehar Poddar, Stephen McCrory, Luigi Penco +3Humanoid Robot ControlHumanoid Robotics

    Florida Institute for Human and Machine Cognition, Pensacola, FL, USA. · University of West Florida, Pensacola, FL, USA.