Learning adaptive planning representations with natural language
guidance
- URL: http://arxiv.org/abs/2312.08566v1
- Date: Wed, 13 Dec 2023 23:35:31 GMT
- Title: Learning adaptive planning representations with natural language
guidance
- Authors: Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S. Siegel, Jiahai
Feng, Noa Korneev, Joshua B. Tenenbaum, Jacob Andreas
- Abstract summary: 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.
- Score: 90.24449752926866
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Effective planning in the real world requires not only world knowledge, but
the ability to leverage that knowledge to build the right representation of the
task at hand. Decades of hierarchical planning techniques have used
domain-specific temporal action abstractions to support efficient and accurate
planning, almost always relying on human priors and domain knowledge to
decompose hard tasks into smaller subproblems appropriate for a goal or set of
goals. This paper describes Ada (Action Domain Acquisition), a framework for
automatically constructing task-specific planning representations using
task-general background knowledge from language models (LMs). Starting with a
general-purpose hierarchical planner and a low-level goal-conditioned policy,
Ada interactively learns a library of planner-compatible high-level action
abstractions and low-level controllers adapted to a particular domain of
planning tasks. On two language-guided interactive planning benchmarks (Mini
Minecraft and ALFRED Household Tasks), Ada strongly outperforms other
approaches that use LMs for sequential decision-making, offering more accurate
plans and better generalization to complex tasks.
Related papers
- Zero-shot Robotic Manipulation with Language-guided Instruction and Formal Task Planning [16.89900521727246]
We propose an innovative language-guided symbolic task planning (LM-SymOpt) framework with optimization.
It is the first expert-free planning framework since we combine the world knowledge from Large Language Models with formal reasoning.
Our experimental results show that LM-SymOpt outperforms existing LLM-based planning approaches.
arXiv Detail & Related papers (2025-01-25T13:33:22Z) - Ontology-driven Prompt Tuning for LLM-based Task and Motion Planning [0.20940572815908076]
Task and Motion Planning (TAMP) approaches combine high-level symbolic plan with low-level motion planning.
LLMs are transforming task planning by offering natural language as an intuitive and flexible way to describe tasks.
This work proposes a novel prompt-tuning framework that employs knowledge-based reasoning to refine and expand user prompts.
arXiv Detail & Related papers (2024-12-10T13:18:45Z) - LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning [7.36760703426119]
This survey aims to highlight the existing challenges in planning with language models.
It focuses on key areas such as embodied environments, optimal scheduling, competitive and cooperative games, task decomposition, reasoning, and planning.
arXiv Detail & Related papers (2024-09-03T11:39:52Z) - Ask-before-Plan: Proactive Language Agents for Real-World Planning [68.08024918064503]
Proactive Agent Planning requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction.
We propose a novel multi-agent framework, Clarification-Execution-Planning (textttCEP), which consists of three agents specialized in clarification, execution, and planning.
arXiv Detail & Related papers (2024-06-18T14:07:28Z) - Embodied Instruction Following in Unknown Environments [66.60163202450954]
We propose an embodied instruction following (EIF) method for complex tasks in the unknown environment.
We build a hierarchical embodied instruction following framework including the high-level task planner and the low-level exploration controller.
For the task planner, we generate the feasible step-by-step plans for human goal accomplishment according to the task completion process and the known visual clues.
arXiv Detail & Related papers (2024-06-17T17:55:40Z) - Interactive Task Planning with Language Models [89.5839216871244]
An interactive robot framework accomplishes long-horizon task planning and can easily generalize to new goals and distinct tasks, even during execution.
Recent large language model based approaches can allow for more open-ended planning but often require heavy prompt engineering or domain specific pretrained models.
We propose a simple framework that achieves interactive task planning with language models by incorporating both high-level planning and low-level skill execution.
arXiv Detail & Related papers (2023-10-16T17:59:12Z) - Robot Task Planning Based on Large Language Model Representing Knowledge
with Directed Graph Structures [2.3698227130544547]
We propose a task planning method that combines human expertise with an LLM and have designed an LLM prompt template, Think_Net_Prompt.
We further propose a method to progressively decompose tasks and generate a task tree to reduce the planning volume for each task.
arXiv Detail & Related papers (2023-06-08T13:10:00Z) - 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) - Procedures as Programs: Hierarchical Control of Situated Agents through
Natural Language [81.73820295186727]
We propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control.
We instantiate this framework on the IQA and ALFRED datasets for NL instruction following.
arXiv Detail & Related papers (2021-09-16T20:36:21Z) - Robust Hierarchical Planning with Policy Delegation [6.1678491628787455]
We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation.
We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains.
arXiv Detail & Related papers (2020-10-25T04:36:20Z)
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.