Multi-Arm Bin-Picking in Real-Time: A Combined Task and Motion Planning Approach
Ilyes Toumi, Andreas Orthey, Alexander von Rohr, Ngo Anh Vien

TL;DR
This paper presents IMAPIP, an integrated multi-arm bin-picking system that combines task and motion planning to enable real-time cooperation of multiple robots, improving efficiency and robustness in unstructured environments.
Contribution
The paper introduces IMAPIP, a novel integrated pipeline that combines high-level task planning with motion planning for multi-arm bin-picking, demonstrating real-time operation and scalability.
Findings
Geometry-aware policy improves bin-picking time by 28% with two arms.
System is robust to bin and object position changes.
Successfully scales to four robot arms working closely.
Abstract
Automated bin-picking is a prerequisite for fully automated manufacturing and warehouses. To successfully pick an item from an unstructured bin the robot needs to first detect possible grasps for the objects, decide on the object to remove and consequently plan and execute a feasible trajectory to retrieve the chosen object. Over the last years significant progress has been made towards solving these problems. However, when multiple robot arms are cooperating the decision and planning problems become exponentially harder. We propose an integrated multi-arm bin-picking pipeline (IMAPIP), and demonstrate that it is able to reliably pick objects from a bin in real-time using multiple robot arms. IMAPIP solves the multi-arm bin-picking task first at high-level using a geometry-aware policy integrated in a combined task and motion planning framework. We then plan motions consistent with this…
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Taxonomy
TopicsRobot Manipulation and Learning · Advanced Manufacturing and Logistics Optimization · Modular Robots and Swarm Intelligence
