PushingBots: Collaborative Pushing via Neural Accelerated Combinatorial Hybrid Optimization
Zili Tang, Ying Zhang, Meng Guo

TL;DR
This paper presents PushingBots, a novel multi-robot system that uses neural-accelerated hybrid optimization to collaboratively push arbitrary objects in complex environments, improving efficiency and adaptability over existing methods.
Contribution
It introduces a comprehensive framework combining combinatorial hybrid optimization, keyframe-guided hybrid search, and neural acceleration for multi-robot collaborative pushing tasks.
Findings
Effective in pushing diverse objects in cluttered environments
Demonstrates improved efficiency with neural acceleration
Validated through extensive simulations and hardware experiments
Abstract
Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective non-prehensile skill for robots to interact with and further change the environment. Existing work often assumes a set of predefined pushing modes and fixed-shape objects. This work tackles the general problem of controlling a robotic fleet to push collaboratively numerous arbitrary objects to respective destinations, within complex environments of cluttered and movable obstacles. It incorporates several characteristic challenges for multi-robot systems such as online task coordination under large uncertainties of cost and duration, and for contact-rich tasks such as hybrid switching among different contact modes, and under-actuation due to constrained contact forces. The proposed method is…
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Taxonomy
TopicsRobot Manipulation and Learning · Modular Robots and Swarm Intelligence · Robotic Path Planning Algorithms
