Related papers: A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots

A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots

URL: http://arxiv.org/abs/2609.01518v1
Date: Tue, 01 Sep 2026 16:47:00 GMT
Title: A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots
Abstract summary: A useful robot must coordinate locomotion, whole-body motion, perception, contact, and operator supervision.<n>We present a robot-local, runtime-editable behavior authoring and runtime system that addresses these challenges.
Score: 0.767210236542832
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: There is tremendous value in humanoid robots taking on physically demanding, hazardous, and repetitive work in spaces built for humans. However, a useful robot for these spaces must coordinate locomotion, whole-body motion, perception, contact, and operator supervision. We present a robot-local, runtime-editable behavior authoring and runtime system that addresses these challenges. We argue that behavior architecture can be a primary enabler of capability, speed, and reliability, and that runtime editability enables fast behavior creation, adaptation, extension, and combination. Our behavior architecture combines object-centric Affordance Templates, a tree structure that provides organization and logic, and runtime-editable perception through a behavior scene and primitive scene actions. Our operator interface remains continuously synchronized to the robot for runtime authoring, monitoring, and repair. Action primitives execute through a whole-body controller that supports concurrent body motions and walking. Demonstrations of our system cover six task variants on Unitree H1-2 and Alex. We execute a push door traversal in 34 seconds and sort six balls by color in 45 seconds under human disturbance. Timed authoring sessions show scratch creation of new loco-manipulation behaviors and adaptation of existing ones in hours. Comparison against the literature finds our approach to be competitive with recent learned systems.

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