Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability

• URL: http://arxiv.org/abs/2401.07868v1
• Date: Mon, 15 Jan 2024 18:01:59 GMT
• Title: Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability
• Authors: Md Sadman Sakib and Yu Sun
• Abstract summary: The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios.
• Score: 6.4111574364474215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.

Related papers

• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)
• AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation [89.68433168477227]
  Large Language Model (LLM) based agents have garnered significant attention and are becoming increasingly popular. This paper investigates enhancing the planning abilities of LLMs through instruction tuning. To address this limitation, this paper explores the automated synthesis of diverse environments and a gradual range of planning tasks.
  arXiv  Detail & Related papers  (2024-08-01T17:59:46Z)
• DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models [5.385540718118656]
  We introduce DELTA, a novel task planning approach based on Large Language Models (LLMs) By using scene graphs as environment representations within LLMs, DELTA achieves rapid generation of precise planning problem descriptions. We show that DELTA enables an efficient and fully automatic task planning pipeline, achieving higher planning success rates and significantly shorter planning times compared to the state of the art.
  arXiv  Detail & Related papers  (2024-04-04T07:59:24Z)
• Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction [11.614036749291216]
  We introduce a new distributed multi-robot planner called S-ATLAS for Safe plAnning for Teams of Language-instructed AgentS. We show that the proposed planner can achieve user-specified task success rates, assuming successful plan execution. We provide comparative experiments against related works showing that our method is significantly more computational efficient and achieves lower help rates.
  arXiv  Detail & Related papers  (2024-02-23T15:02:44Z)
• Tree-Planner: Efficient Close-loop Task Planning with Large Language Models [63.06270302774049]
  Tree-Planner reframes task planning with Large Language Models into three distinct phases. Tree-Planner achieves state-of-the-art performance while maintaining high efficiency.
  arXiv  Detail & Related papers  (2023-10-12T17:59:50Z)
• Guiding Language Model Reasoning with Planning Tokens [122.43639723387516]
  Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks. We propose a hierarchical generation scheme to encourage a more structural generation of chain-of-thought steps. Our approach requires a negligible increase in trainable parameters (0.001%) and can be applied through either full fine-tuning or a more parameter-efficient scheme.
  arXiv  Detail & Related papers  (2023-10-09T13:29:37Z)
• AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
  Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
  arXiv  Detail & Related papers  (2023-05-26T05:52:27Z)
• Understanding the Capabilities of Large Language Models for Automated Planning [24.37599752610625]
  The study seeks to shed light on the capabilities of LLMs in solving complex planning problems. It provides insights into the most effective approaches for using LLMs in this context.
  arXiv  Detail & Related papers  (2023-05-25T15:21:09Z)
• Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning [45.51792981370957]
  We investigate the applicability of a smaller class of large language models (LLMs) in robotic task planning by learning to decompose tasks into subgoal specifications for a planner to execute sequentially. Our method grounds the input of the LLM on the domain that is represented as a scene graph, enabling it to translate human requests into executable robot plans. Our findings suggest that the knowledge stored in an LLM can be effectively grounded to perform long-horizon task planning, demonstrating the promising potential for the future application of neuro-symbolic planning methods in robotics.
  arXiv  Detail & Related papers  (2023-05-12T18:14:32Z)
• A Framework for Neurosymbolic Robot Action Planning using Large Language Models [3.0501524254444767]
  We present a framework aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs) into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL) Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.
  arXiv  Detail & Related papers  (2023-03-01T11:54:22Z)

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.