Synergizing Efficiency and Reliability for Continuous Mobile Manipulation

2026-04-07

AI Summary

This work addresses the inherent conflict between planning and reactive control in continuous robotic manipulation, where efficiency and reliability are often difficult to balance. The authors propose a unified framework that, for the first time, integrates reliability awareness into spatiotemporal trajectory optimization to generate globally consistent paths that jointly optimize efficiency and robustness. A phase-dependent switching controller is introduced to adaptively alternate between trajectory tracking and error compensation, enabling online replanning. Combined with a hierarchical initialization strategy, the system achieves a 26.67%–81.67% improvement in task success rate over existing methods in real-world environments and demonstrates strong generalization across diverse end-effector constraint scenarios.

Abstract

Humans seamlessly fuse anticipatory planning with immediate feedback to perform successive mobile manipulation tasks without stopping, achieving both high efficiency and reliability. Replicating this fluid and reliable behavior in robots remains fundamentally challenging, not only due to conflicts between long-horizon planning and real-time reactivity, but also because excessively pursuing efficiency undermines reliability in uncertain environments: it impairs stable perception and the potential for compensation, while also increasing the risk of unintended contact. In this work, we present a unified framework that synergizes efficiency and reliability for continuous mobile manipulation. It features a reliability-aware trajectory planner that embeds essential elements for reliable execution into spatiotemporal optimization, generating efficient and reliability-promising global trajectories. It is coupled with a phase-dependent switching controller that seamlessly transitions between global trajectory tracking for efficiency and task-error compensation for reliability. We also investigate a hierarchical initialization that facilitates online replanning despite the complexity of long-horizon planning problems. Real-world evaluations demonstrate that our approach enables efficient and reliable completion of successive tasks under uncertainty (e.g., dynamic disturbances, perception and control errors). Moreover, the framework generalizes to tasks with diverse end-effector constraints. Compared with state-of-the-art baselines, our method consistently achieves the highest efficiency while improving the task success rate by 26.67\%--81.67\%. Comprehensive ablation studies further validate the contribution of each component. The source code will be released.

Problem

• mobile manipulation
• efficiency
• reliability
• uncertainty
• continuous tasks

Innovation

• reliability-aware planning
• continuous mobile manipulation
• phase-dependent switching control
• online replanning
• spatiotemporal trajectory optimization