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

2024-10-21

arXiv.org

AI Summary

In real-world settings, single-robot object manipulation suffers from poor robustness due to perceptual limitations and physical uncertainty. Method: This paper introduces “Temporal Caging”—a novel framework that extends classical spatial caging to spatiotemporal constraints. It synthesizes an equivalent caging structure through time-varying end-effector configurations, operating open-loop without real-time perception, precise object modeling, or prior knowledge of object geometry or physical properties. The approach unifies sensor-agnostic manipulation, uncertainty propagation modeling, quasi-static/dynamic motion planning, and temporal configuration folding scheduling, supporting both geometric and energetic caging formulations. Results: Experiments demonstrate high robustness and precision in challenging manipulation tasks—including non-prehensile transport, reorientation, and dynamic trapping—under severe sensory constraints. The framework serves as a plug-and-play enhancement module for perception-limited robotic systems.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Robust object manipulation under uncertainties and perception limits
• Forming caging configurations with single robot engagement
• Open-loop manipulation without detailed object knowledge or real-time feedback

Innovation

• Single-robot caging via temporal configuration switching
• Dynamic cage formation without multiple contacts
• Open-loop manipulation under uncertainty and perception limits