Structurally guided task decomposition in spatial navigation tasks
- URL: http://arxiv.org/abs/2310.02221v1
- Date: Tue, 3 Oct 2023 17:27:30 GMT
- Title: Structurally guided task decomposition in spatial navigation tasks
- Authors: Ruiqi He, Carlos G. Correa, Thomas L. Griffiths, Mark K. Ho
- Abstract summary: We extend an existing model of human task decomposition to explain a wide range of simple planning problems.
Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.
- Score: 7.21356271882087
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: How are people able to plan so efficiently despite limited cognitive
resources? We aimed to answer this question by extending an existing model of
human task decomposition that can explain a wide range of simple planning
problems by adding structure information to the task to facilitate planning in
more complex tasks. The extended model was then applied to a more complex
planning domain of spatial navigation. Our results suggest that our framework
can correctly predict the navigation strategies of the majority of the
participants in an online experiment.
Related papers
- Plan-MCTS: Plan Exploration for Action Exploitation in Web Navigation [50.406803870992974]
Plan-MCTS is a framework that reformulates web navigation by shifting exploration to a semantic Plan Space.<n>Plan-MCTS achieves state-of-the-art performance, surpassing current approaches with higher task effectiveness and search efficiency.
arXiv Detail & Related papers (2026-02-15T10:24:45Z) - TodoEvolve: Learning to Architect Agent Planning Systems [68.48983335970901]
TodoEvolve is a meta-planning paradigm that autonomously synthesizes and dynamically revises task-specific planning.<n>PlanFactory provides a common interface for heterogeneous planning patterns.<n>TodoEvolve consistently surpasses carefully engineered planning modules while maintaining economical API costs and runtime overhead.
arXiv Detail & Related papers (2026-02-08T06:37:01Z) - HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking [109.09735490692202]
We propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning.<n> Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.
arXiv Detail & Related papers (2025-05-05T02:38:58Z) - Visual Environment-Interactive Planning for Embodied Complex-Question Answering [28.929345360469807]
This study focuses on Embodied Complex-Question Answering task.
The core of this task lies in making appropriate plans based on the perception of the visual environment.
Considering multi-step planning, the framework for formulating plans in a sequential manner is proposed in this paper.
arXiv Detail & Related papers (2025-04-01T13:26:28Z) - A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models [15.874604623294427]
Multi-Phases planning problem involves multiple interconnected stages, such as outlining, information gathering, and planning.
Existing reasoning approaches have struggled to effectively address this complex task.
Our research aims to address this challenge by developing a human-like planning framework for LLM agents.
arXiv Detail & Related papers (2024-05-28T14:13:32Z) - 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 Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making [9.302910360945042]
Planning with prior knowledge extracted from complicated real-world tasks is crucial for humans to make accurate decisions.
We introduce a multiple-encoder and individual-predictor regime to learn task-essential representations from sufficient data for simple subtasks.
We also use the attention mechanism to generate a top-k subtask planning tree, which customizes subtask execution plans in guiding complex decisions on unseen tasks.
arXiv Detail & Related papers (2023-12-18T09:00:31Z) - 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) - 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) - Hierarchical Imitation Learning with Vector Quantized Models [77.67190661002691]
We propose to use reinforcement learning to identify subgoals in expert trajectories.
We build a vector-quantized generative model for the identified subgoals to perform subgoal-level planning.
In experiments, the algorithm excels at solving complex, long-horizon decision-making problems outperforming state-of-the-art.
arXiv Detail & Related papers (2023-01-30T15:04:39Z) - Generalization with Lossy Affordances: Leveraging Broad Offline Data for
Learning Visuomotor Tasks [65.23947618404046]
We introduce a framework that acquires goal-conditioned policies for unseen temporally extended tasks via offline reinforcement learning on broad data.
When faced with a novel task goal, the framework uses an affordance model to plan a sequence of lossy representations as subgoals that decomposes the original task into easier problems.
We show that our framework can be pre-trained on large-scale datasets of robot experiences from prior work and efficiently fine-tuned for novel tasks, entirely from visual inputs without any manual reward engineering.
arXiv Detail & Related papers (2022-10-12T21:46:38Z) - Fast Inference and Transfer of Compositional Task Structures for
Few-shot Task Generalization [101.72755769194677]
We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph.
Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks.
Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
arXiv Detail & Related papers (2022-05-25T10:44:25Z) - Differentiable Spatial Planning using Transformers [87.90709874369192]
We propose Spatial Planning Transformers (SPT), which given an obstacle map learns to generate actions by planning over long-range spatial dependencies.
In the setting where the ground truth map is not known to the agent, we leverage pre-trained SPTs in an end-to-end framework.
SPTs outperform prior state-of-the-art differentiable planners across all the setups for both manipulation and navigation tasks.
arXiv Detail & Related papers (2021-12-02T06:48:16Z) - Task Scoping: Generating Task-Specific Abstractions for Planning [19.411900372400183]
Planning to solve any specific task using an open-scope world model is computationally intractable.
We propose task scoping: a method that exploits knowledge of the initial condition, goal condition, and transition-dynamics structure of a task.
We prove that task scoping never deletes relevant factors or actions, characterize its computational complexity, and characterize the planning problems for which it is especially useful.
arXiv Detail & Related papers (2020-10-17T21:19:25Z) - Flexible and Efficient Long-Range Planning Through Curious Exploration [13.260508939271764]
We show that the Curious Sample Planner can efficiently discover temporally-extended plans for solving a wide range of physically realistic 3D tasks.
In contrast, standard planning and learning methods often fail to solve these tasks at all or do so only with a huge and highly variable number of training samples.
arXiv Detail & Related papers (2020-04-22T21:47:29Z)
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.