Caging in Time: A Framework for Robust Object Manipulation under Uncertainties and Limited Robot Perception
Gaotian Wang, Kejia Ren, Andrew S. Morgan, Kaiyu Hang

TL;DR
This paper introduces Caging in Time, a novel framework enabling robust object manipulation with a single robot by dynamically forming cages through strategic configuration switching, overcoming perception and geometric limitations.
Contribution
It proposes a new approach that allows caging configurations to be formed with one robot by temporal switching, expanding applicability beyond traditional multi-robot or geometry-dependent methods.
Findings
Successfully applied to quasi-static and dynamic tasks
Achieves robust open-loop manipulation without detailed object knowledge
Compatible with geometry-based and energy-based cages
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Reinforcement Learning in Robotics
