High-Performance Dual-Arm Task and Motion Planning for Tabletop Rearrangement

2025-12-08

AI Summary

Desktop rearrangement tasks pose significant challenges due to highly entangled initial and goal object configurations, non-monotonicity, and the need for long-horizon, coordinated multi-arm planning. Method: This paper proposes SDAR—a tightly coupled task-and-motion planning framework—built upon dependency-driven task decomposition, hierarchical synchronized dual-arm motion planning, and a GPU-accelerated SIMD motion planner. It introduces a novel dependency graph decomposition algorithm to enable temporal coordination and joint optimization of multi-arm actions. Contribution/Results: SDAR achieves 100% planning success rate in strongly entangled scenarios, substantially outperforming existing state-of-the-art methods. The framework is rigorously validated on two physical UR-5e robotic arms, demonstrating robust real-world performance and reliability.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We propose Synchronous Dual-Arm Rearrange- ment Planner (SDAR), a task and motion planning (TAMP) framework for tabletop rearrangement, where two robot arms equipped with 2-finger grippers must work together in close proximity to rearrange objects whose start and goal config- urations are strongly entangled. To tackle such challenges, SDAR tightly knit together its dependency-driven task planner (SDAR-T) and synchronous dual-arm motion planner (SDAR- M), to intelligently sift through a large number of possible task and motion plans. Specifically, SDAR-T applies a simple yet effective strategy to decompose the global object dependency graph induced by the rearrangement task, to produce more optimal dual-arm task plans than solutions derived from optimal task plans for a single arm. Leveraging state-of-the-art GPU SIMD-based motion planning tools, SDAR-M employs a layered motion planning strategy to sift through many task plans for the best synchronous dual-arm motion plan while ensuring high levels of success rate. Comprehensive evaluation demonstrates that SDAR delivers a 100% success rate in solving complex, non-monotone, long-horizon tabletop rearrangement tasks with solution quality far exceeding the previous state- of-the-art. Experiments on two UR-5e arms further confirm SDAR directly and reliably transfers to robot hardware.

Problem

• Develops a dual-arm task and motion planning framework for tabletop rearrangement
• Addresses challenges of entangled object configurations requiring close dual-arm cooperation
• Ensures high success rates and optimal solutions for complex, non-monotone tasks

Innovation

• Dual-arm task planner decomposes object dependency graphs
• Layered motion planning uses GPU SIMD tools for synchronization
• Framework ensures 100% success in complex tabletop rearrangement tasks