Original title: A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots
Duncan Calvert, Luigi Penco, Dexton Anderson, Tomasz Bialek +3 · September 2026
Humanoid robots need to handle tough, repetitive work in human spaces, but combining walking, moving, seeing, and touching is difficult. To fix this, researchers built a system that lets operators create and edit robot behaviors while the robot runs. The system connects several tools together. It uses templates centered on objects, a tree structure for logic, and an editable perception setup. A whole-body controllerA system that coordinates all parts of the robot's body for simultaneous actions like walking and manipulating. then handles walking and moving body parts at the same time, while an operator stays linked to the robot to monitor and fix tasks. Testing on two robots showed strong results across six task variants. One robot walked through a push door in 34 seconds. Another sorted six balls by color in 45 seconds while humans disturbed it. People also authored new behaviors from scratch in hours.
Imagine you are cooking a new recipe from memory. Partway through, you realize you forgot an ingredient, so you have to improvise and change the dish on the fly. If you couldn't adjust mid-cooking, the meal might fail. In this paper, the robot faces changing tasks and disturbances, and the system allows it to adapt its behavior in real-time, like adjusting a recipe while cooking.
This matters because humanoid robots could take on dangerous jobs like handling hazardous materials or repetitive assembly, making workplaces safer for humans and reducing labor strain.
The abstract does not explicitly state limitations, but the system is demonstrated on two specific robots and six task variants, so its generalizability to other environments or tasks isn't established.
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A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots — Duncan Calvert, Luigi Penco, Dexton Anderson et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.