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arXiv · 2410.16481

Caging in Time: A Framework for Robust Object Manipulation under Uncertainties and Limited Robot Perception

Gaotian Wang·Kejia Ren·Andrew S. Morgan·Kaiyu Hang
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Abstract

Real-world object manipulation has been commonly challenged by physical uncertainties and perception limitations. Being an effective strategy, while caging configuration-based manipulation frameworks have successfully provided robust solutions, they are not broadly applicable due to their strict requirements on the availability of multiple robots, widely distributed contacts, or specific geometries of robots or objects. Building upon previous sensorless manipulation ideas and uncertainty handling approaches, this work proposes a novel framework termed Caging in Time to allow caging configurations to be formed even with one robot engaged in a task. This concept leverages the insight that while caging requires constraining the object's motion, only part of the cage actively contacts the object at any moment. As such, by strategically switching the end-effector configuration and collapsing it in time, we form a cage with its necessary portion active whenever needed. We instantiate our approach on challenging quasi-static and dynamic manipulation tasks, showing that Caging in Time can be achieved in general cage formulations including geometry-based and energy-based cages. With extensive experiments, we show robust and accurate manipulation, in an open-loop manner, without requiring detailed knowledge of the object geometry or physical properties, or real-time accurate feedback on the manipulation states. In addition to being an effective and robust open-loop manipulation solution, Caging in Time can be a supplementary strategy to other manipulation systems affected by uncertain or limited robot perception.

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Gaotian Wang, Kejia Ren, Andrew S. Morgan, Kaiyu Hang. 2024-10-21. Caging in Time: A Framework for Robust Object Manipulation under Uncertainties and Limited Robot Perception. https://arxiv.org/abs/2410.16481

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