Matan Sudry, Tom Jurgenson, Erez Karpas

Task and Motion Planning Using Infinite Completion Tree and Agnostic Skills

Introduction

Task and motion planning using infinite completion tree and agnostic skills. Enhance robotic task & motion planning (TAMP) with a hierarchical framework using STAP, ELS, and a novel success estimator. Improves efficiency and reliability for complex, long-horizon manipulation tasks.

Abstract

This work builds upon existing task and motion planning (TAMP) frameworks by integrating pre-trained Sequencing Task-Agnostic Policies (STAP) and Effort Level Search (ELS) to create a hierarchical approach that decouples high-level task decisions from low-level motion execution. The method enhances the planning process by incorporating a novel success rate estimator (P ), which provides more accurate task success predictions than traditional Q-value estimators. We formalize the problem of long-horizon manipulation tasks, where high-level decisions are made in discrete spaces and low-level actions are executed in continuous space. To guide the search process efficiently, we leverage the infinite completion tree structure of ELS, which dynamically adjusts computational resources based on task complexity. Empirical results demonstrate that our approach significantly improves planning efficiency and execution reliability, outperforming traditional methods by reducing the search space and computational overhead. Our work highlights the effectiveness of combining learned skills from STAP with ELS and P in a hierarchical structure, laying the foundation for scalable robotic planning in complex, real-world manipulation tasks.

Review

This paper presents a compelling advancement in Task and Motion Planning (TAMP) by introducing a hierarchical framework designed to tackle long-horizon manipulation tasks effectively. The core innovation lies in its ability to decouple high-level task decisions, made in discrete spaces, from low-level motion execution, performed in continuous spaces. By integrating pre-trained Sequencing Task-Agnostic Policies (STAP) for learned skills, an Effort Level Search (ELS) mechanism for efficient planning, and a novel success rate estimator (P), the work promises significant improvements. The abstract highlights empirical evidence of enhanced planning efficiency and execution reliability, positioning this method as a superior alternative to traditional TAMP approaches. A significant strength of this work is the intelligent combination of complementary techniques. The pre-trained STAP provides a robust foundation of "agnostic skills," suggesting a degree of generalizability that can accelerate learning and execution across various tasks. The ELS, guided by an infinite completion tree structure, represents a sophisticated search strategy that dynamically allocates computational resources, ensuring efficient exploration of the solution space. Furthermore, the introduction of the P estimator, touted for its superior accuracy over traditional Q-values, is crucial for making more informed and reliable decisions during the planning process, directly contributing to the method's claimed improvements in task success prediction. This integrated approach directly addresses the challenges of large search spaces and computational complexity inherent in complex manipulation tasks. While the presented approach appears highly promising for scalable robotic planning, the abstract leaves some areas for further exploration. It would be beneficial for the full paper to elaborate on the exact nature and limitations of the "agnostic skills" from STAP – for instance, their transferability to truly novel scenarios or potential trade-offs in optimality for highly specialized tasks. Additionally, while efficiency and reliability are thoroughly addressed, discussions on the optimality of the generated plans and robustness to real-world sensor noise or unexpected environmental changes would strengthen the overall contribution. Nevertheless, this work lays a strong foundation by demonstrating a powerful synergy between learned policies and sophisticated search techniques, marking a notable step forward for TAMP in complex, real-world robotic applications.

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