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Toward Shared Control in Clutter
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DOI：10.1109/lra.2026.3723281.png)
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Abstract
En 中文
Assistive robots can enable people with motor impairments to complete everyday tasks independently, offering active, semi-autonomous support. However, providing correct and seamless assistance in more cluttered multi-task environments remains a key open challenge. To address this challenge, we introduce a task-space-dependent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clutteredness</i> metric that quantifies scene complexity considering all tasks in the scene. This metric allows us to adapt shared control skills based on the scene’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clutteredness</i> with our new skill adaptation method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShaCC</i> (Shared Control in Clutter). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShaCC</i> comprises two steps: first, we optimize task space parameters to reduce task space overlap (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shrinking</i>), and second, provide early orientation support toward the whole scene (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Merging</i>). We apply <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShaCC</i> to the Shared Control Template (SCT) framework for experimental evaluation. Our results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clutteredness</i> metric is an expressive measure of scene complexity. Further, the task-aware skill representation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShaCC</i> improves assistance in multi-task environments with higher success rates and smoother task switching during manipulation. We show this in simulation with up to ten tasks per scene, improving the success rate by 45% on average, and validate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShaCC</i> in a pilot study with three users on the assistive robot MAYA, achieving 100% task success in a real-world scene with five tasks.
Keywords:
Physically assistive devices
human-centered robotics
intention recognition
shared control
cluttered environments
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