CAPE: Corrective Actions from Precondition Errors using Large Language Models

• URL: http://arxiv.org/abs/2211.09935v3
• Date: Sat, 9 Mar 2024 13:53:47 GMT
• Title: CAPE: Corrective Actions from Precondition Errors using Large Language Models
• Authors: Shreyas Sundara Raman, Vanya Cohen, Ifrah Idrees, Eric Rosen, Ray Mooney, Stefanie Tellex and David Paulius
• Abstract summary: We propose a novel approach that attempts to propose corrective actions to resolve precondition errors during planning. CAPE improves the quality of generated plans by leveraging few-shot reasoning from action preconditions. Our improvements transfer to a Boston Dynamics Spot robot with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76.49% compared to SayCan.
• Score: 8.547766794082184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting commonsense knowledge from a large language model (LLM) offers a path to designing intelligent robots. Existing approaches that leverage LLMs for planning are unable to recover when an action fails and often resort to retrying failed actions, without resolving the error's underlying cause. We propose a novel approach (CAPE) that attempts to propose corrective actions to resolve precondition errors during planning. CAPE improves the quality of generated plans by leveraging few-shot reasoning from action preconditions. Our approach enables embodied agents to execute more tasks than baseline methods while ensuring semantic correctness and minimizing re-prompting. In VirtualHome, CAPE generates executable plans while improving a human-annotated plan correctness metric from 28.89% to 49.63% over SayCan. Our improvements transfer to a Boston Dynamics Spot robot initialized with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76.49% compared to SayCan. Our approach enables the robot to follow natural language commands and robustly recover from failures, which baseline approaches largely cannot resolve or address inefficiently.

Related papers

• Mining Action Rules for Defect Reduction Planning [14.40839500239476]
  We introduce CounterACT, a Counterfactual ACTion rule mining approach that can generate defect reduction plans without black-box models. We compare the effectiveness of CounterACT with the original action rule mining algorithm and six established defect reduction approaches on 9 software projects. Our results show that, compared to competing approaches, CounterACT's explainable plans achieve higher overlap scores at the release level.
  arXiv  Detail & Related papers  (2024-05-22T15:31:09Z)
• Yell At Your Robot: Improving On-the-Fly from Language Corrections [84.09578841663195]
  We show that high-level policies can be readily supervised with human feedback in the form of language corrections. This framework enables robots not only to rapidly adapt to real-time language feedback, but also incorporate this feedback into an iterative training scheme.
  arXiv  Detail & Related papers  (2024-03-19T17:08:24Z)
• Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach [55.613461060997004]
  Large Language Models (LLMs) have catalyzed transformative advances across a spectrum of natural language processing tasks. We propose an innovative textitmetacognitive approach, dubbed textbfCLEAR, to equip LLMs with capabilities for self-aware error identification and correction.
  arXiv  Detail & Related papers  (2024-03-08T19:18:53Z)
• 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)
• Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity [0.659529078336196]
  Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions. LLMs hallucination may result in robots confidently executing plans that are misaligned with user goals or even unsafe in critical scenarios. We propose introspective planning, a systematic approach that align LLM's uncertainty with the inherent ambiguity of the task.
  arXiv  Detail & Related papers  (2024-02-09T16:40:59Z)
• Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability [6.4111574364474215]
  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.
  arXiv  Detail & Related papers  (2024-01-15T18:01:59Z)
• Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents [99.17668730578586]
  Pre-trained large language models (LLMs) capture procedural knowledge about the world. Plan, Eliminate, and Track (PET) framework translates a task description into a list of high-level sub-tasks. PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.
  arXiv  Detail & Related papers  (2023-05-03T20:11:22Z)
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models [68.57918965060787]
  Large language models (LLMs) can be used to score potential next actions during task planning. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
  arXiv  Detail & Related papers  (2022-09-22T20:29:49Z)
• Correcting Robot Plans with Natural Language Feedback [88.92824527743105]
  We explore natural language as an expressive and flexible tool for robot correction. We show that these transformations enable users to correct goals, update robot motions, and recover from planning errors. Our method makes it possible to compose multiple constraints and generalizes to unseen scenes, objects, and sentences in simulated environments and real-world environments.
  arXiv  Detail & Related papers  (2022-04-11T15:22:43Z)
• Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents [111.33545170562337]
  We investigate the possibility of grounding high-level tasks, expressed in natural language, to a chosen set of actionable steps. We find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into low-level plans. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions.
  arXiv  Detail & Related papers  (2022-01-18T18:59:45Z)

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.