A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches
- URL: http://arxiv.org/abs/2404.02817v4
- Date: Sun, 30 Jun 2024 23:56:53 GMT
- Title: A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches
- Authors: Zhigen Zhao, Shuo Cheng, Yan Ding, Ziyi Zhou, Shiqi Zhang, Danfei Xu, Ye Zhao,
- Abstract summary: Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to reason over long-horizon, dynamic tasks.
This survey provides a comprehensive review on optimization-based TAMP, covering (i) planning domain representations, (ii) individual solution strategies for components, including AI planning and trajectory optimization (TO), and (iii) the dynamic interplay between logic-based task planning and model-based TO.
- Score: 15.136760934936381
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
We construct a benchmark suite encompassing both classical planning domains and natural language scenarios.
Second, we investigate the use of in-context learning (ICL) to enhance LLM planning, exploring the direct relationship between increased context length and improved planning performance.
Third, we demonstrate the positive impact of fine-tuning LLMs on optimal planning paths, as well as the effectiveness of incorporating model-driven search procedures.
arXiv Detail & Related papers (2024-06-18T22:57:06Z) - Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making [6.867637277944729]
Transformer-Enhanced Motion Planner (TEMP) is a novel deep learning-based motion planning framework.
TEMP synergizes an Environmental Information Semantic (EISE) with a Motion Planning Transformer (MPT)
arXiv Detail & Related papers (2024-04-30T09:48:11Z) - LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning [78.2390460278551]
Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation.
Here, we present LLM3, a novel Large Language Model (LLM)-based TAMP framework featuring a domain-independent interface.
Specifically, we leverage the powerful reasoning and planning capabilities of pre-trained LLMs to propose symbolic action sequences and select continuous action parameters for motion planning.
arXiv Detail & Related papers (2024-03-18T08:03:47Z) - Unified Task and Motion Planning using Object-centric Abstractions of
Motion Constraints [56.283944756315066]
We propose an alternative TAMP approach that unifies task and motion planning into a single search.
Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
arXiv Detail & Related papers (2023-12-29T14:00:20Z) - Learning adaptive planning representations with natural language
guidance [90.24449752926866]
This paper describes Ada, a framework for automatically constructing task-specific planning representations.
Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks.
arXiv Detail & Related papers (2023-12-13T23:35:31Z) - EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought [95.37585041654535]
Embodied AI is capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments.
In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI.
Experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering.
arXiv Detail & Related papers (2023-05-24T11:04:30Z) - Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion
Planning [36.300564378022315]
We present a learning-enabled Task and Motion Planning (TAMP) algorithm for solving mobile manipulation problems in environments with many articulated and movable obstacles.
The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan.
arXiv Detail & Related papers (2022-11-03T04:12:04Z) - Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in
Latent Space [76.46113138484947]
General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments.
To address this issue, goal-conditioned reinforcement learning aims to acquire policies that can reach goals for a wide range of tasks on command.
We propose Planning to Practice, a method that makes it practical to train goal-conditioned policies for long-horizon tasks.
arXiv Detail & Related papers (2022-05-17T06:58:17Z) - Neural Motion Planning for Autonomous Parking [6.1805402105389895]
This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method.
The proposed method effectively learns the representations of a given state, and shows improvement in terms of algorithm performance.
arXiv Detail & Related papers (2021-11-12T14:29:38Z) - Learning Symbolic Operators for Task and Motion Planning [29.639902380586253]
integrated task and motion planners (TAMP) handle the complex interaction between motion-level decisions and task-level plan feasibility.
TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient.
We propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system.
arXiv Detail & Related papers (2021-02-28T19:08:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.