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A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches

Zhigen Zhao, Shuo Cheng, Yan Ding, Ziyi Zhou, Shiqi Zhang, Danfei Xu, Ye Zhao

TL;DR

This survey provides a comprehensive review on optimization-based TAMP, covering first, planning domain representations, including action description languages and temporal logic, second, individual solution strategies for components of TAMP, including AI planning and trajectory optimization (TO), and finally, the dynamic interplay between logic-based task planning and model-based TO.

Abstract

Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic tasks. Optimization-based TAMP focuses on hybrid optimization approaches that define goal conditions via objective functions and are capable of handling open-ended goals, robotic dynamics, and physical interaction between the robot and the environment. Therefore, optimization-based TAMP is particularly suited to solve highly complex, contact-rich locomotion and manipulation problems. This survey provides a comprehensive review on optimization-based TAMP, covering (i) planning domain representations, including action description languages and temporal logic, (ii) individual solution strategies for components of TAMP, including AI planning and trajectory optimization (TO), and (iii) the dynamic interplay between logic-based task planning and model-based TO. A particular focus of this survey is to highlight the algorithm structures to efficiently solve TAMP, especially hierarchical and distributed approaches. Additionally, the survey emphasizes the synergy between the classical methods and contemporary learning-based innovations such as large language models. Furthermore, the future research directions for TAMP is discussed in this survey, highlighting both algorithmic and application-specific challenges.

A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches

TL;DR

This survey provides a comprehensive review on optimization-based TAMP, covering first, planning domain representations, including action description languages and temporal logic, second, individual solution strategies for components of TAMP, including AI planning and trajectory optimization (TO), and finally, the dynamic interplay between logic-based task planning and model-based TO.

Abstract

Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic tasks. Optimization-based TAMP focuses on hybrid optimization approaches that define goal conditions via objective functions and are capable of handling open-ended goals, robotic dynamics, and physical interaction between the robot and the environment. Therefore, optimization-based TAMP is particularly suited to solve highly complex, contact-rich locomotion and manipulation problems. This survey provides a comprehensive review on optimization-based TAMP, covering (i) planning domain representations, including action description languages and temporal logic, (ii) individual solution strategies for components of TAMP, including AI planning and trajectory optimization (TO), and (iii) the dynamic interplay between logic-based task planning and model-based TO. A particular focus of this survey is to highlight the algorithm structures to efficiently solve TAMP, especially hierarchical and distributed approaches. Additionally, the survey emphasizes the synergy between the classical methods and contemporary learning-based innovations such as large language models. Furthermore, the future research directions for TAMP is discussed in this survey, highlighting both algorithmic and application-specific challenges.
Paper Structure (35 sections, 10 equations, 12 figures, 3 tables)

This paper contains 35 sections, 10 equations, 12 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Survey Goals and Roadmap
  3. Preliminaries
  4. Assumptions
  5. Task and Motion Planning as Joint Optimization
  6. Motivating Example
  7. Planning Domain Representation
  8. AI Planning
  9. Temporal Logic
  10. Learning Operators and State Abstractions
  11. Generating Domain Knowledge by LLMs
  12. Task Planning
  13. Classical Task Planning
  14. Learning Models for Task Planning
  15. LLMs for Task Planning
  16. ...and 20 more sections

Figures (12)

  • Figure 1: Optimization-based TAMP enables dynamic locomotion and manipulation behaviors in complex environments: (a) bipedal robot loco-manipulation adu2022optimal; (b) mobile robot table-top manipulation garrett2021integrated; (c) long-horizon multi-agent collaboration envall2023differentiable.
  • Figure 2: Overview of the problem structures and related algorithms in optimization-based TAMP discussed in this survey paper.
  • Figure 3: Percentage statistics sorted by countries of origin. 182 references that directly address TAMP are included.
  • Figure 4: An illustration for the table top manipulation example: (a) initial state (b) one possible final state.
  • Figure 5: Tabletop manipulation example expressed in STL.
  • ...and 7 more figures