Real‐Time Control of a Humanoid Robot for Whole‐Body Tactile Interaction

Enabling robots to interact physically with complex, unstructured environments remains a significant challenge. Methods relying solely on joint‐torque sensing suffer from ambiguity in multicontact scenarios, while vision is prone to occlusion. This article presents an approach using a whole‐body tactile skin sensor network to address these limitations by integrating feedback for compliance, force control, and collision avoidance. The control framework uses quadratic programming to integrate rich tactile and proximity feedback from the skin network. To maintain real‐time performance with this dense sensory data, the method clusters sensor activations into active regions. This enables the robot to generate whole‐body compliance, regulate interaction forces across various body parts, and transform proximity feedback into distance constraints to dynamically avoid collisions in unmodeled environments. The effectiveness and real‐time feasibility of this approach are demonstrated through experiments on a humanoid robot performing tasks such as transporting bulky objects, controlling interaction forces, and avoiding dynamic obstacles.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2025-07-13
DOI
https://doi.org/10.1002/aisy.202500149
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.00

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article

Real‐Time Control of a Humanoid Robot for Whole‐Body Tactile Interaction

Simon Armleder, Gordon Cheng, Florian Bergner, J. Rogelio Guadarrama-Olvera et al.
Advanced Intelligent Systems
Robot Manipulation and Learning
article

Real‐Time Control of a Humanoid Robot for Whole‐Body Tactile Interaction

Simon Armleder, Gordon Cheng, Florian Bergner, J. Rogelio Guadarrama-Olvera, Jun Nakanishi
article en

Abstract

Enabling robots to interact physically with complex, unstructured environments remains a significant challenge. Methods relying solely on joint‐torque sensing suffer from ambiguity in multicontact scenarios, while vision is prone to occlusion. This article presents an approach using a whole‐body tactile skin sensor network to address these limitations by integrating feedback for compliance, force control, and collision avoidance. The control framework uses quadratic programming to integrate rich tactile and proximity feedback from the skin network. To maintain real‐time performance with this dense sensory data, the method clusters sensor activations into active regions. This enables the robot to generate whole‐body compliance, regulate interaction forces across various body parts, and transform proximity feedback into distance constraints to dynamically avoid collisions in unmodeled environments. The effectiveness and real‐time feasibility of this approach are demonstrated through experiments on a humanoid robot performing tasks such as transporting bulky objects, controlling interaction forces, and avoiding dynamic obstacles.

Advanced Intelligent SystemsVol. 7(12)
Fraunhofer Institute for Cognitive Systems (DE), Meijo University (JP)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 80%
Robot Manipulation and Learning
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