Conditionally Combining Robot Skills using Large Language Models

• URL: http://arxiv.org/abs/2310.17019v1
• Date: Wed, 25 Oct 2023 21:46:34 GMT
• Title: Conditionally Combining Robot Skills using Large Language Models
• Authors: K.R. Zentner, Ryan Julian, Brian Ichter, Gaurav S. Sukhatme
• Abstract summary: We introduce an extension of the Meta-World benchmark, which allows a large language model to operate in a simulated robotic environment. By using the same set of tasks as Meta-World, Language-World results can be easily compared to Meta-World results. Second, we introduce a method we call Plan Conditioned Behavioral Cloning (PCBC), that allows finetuning the behavior of high-level plans using end-to-end demonstrations.
• Score: 21.41468872596801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper combines two contributions. First, we introduce an extension of the Meta-World benchmark, which we call "Language-World," which allows a large language model to operate in a simulated robotic environment using semi-structured natural language queries and scripted skills described using natural language. By using the same set of tasks as Meta-World, Language-World results can be easily compared to Meta-World results, allowing for a point of comparison between recent methods using Large Language Models (LLMs) and those using Deep Reinforcement Learning. Second, we introduce a method we call Plan Conditioned Behavioral Cloning (PCBC), that allows finetuning the behavior of high-level plans using end-to-end demonstrations. Using Language-World, we show that PCBC is able to achieve strong performance in a variety of few-shot regimes, often achieving task generalization with as little as a single demonstration. We have made Language-World available as open-source software at https://github.com/krzentner/language-world/.

Related papers

• Towards Universal Semantics With Large Language Models [4.873927154453253]
  We present the first study of using large language models (LLMs) to generate Natural Semantic Metalanguage explications.<n>Our 1B and 8B models outperform GPT-4o in producing accurate, cross-translatable explications.
  arXiv  Detail & Related papers  (2025-05-17T00:11:58Z)
• CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
  This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
  arXiv  Detail & Related papers  (2024-04-03T02:21:46Z)
• Language Models are Universal Embedders [48.12992614723464]
  We show that pre-trained transformer decoders can embed universally when finetuned on limited English data. Our models achieve competitive performance on different embedding tasks by minimal training data. These results provide evidence of a promising path towards building powerful unified embedders.
  arXiv  Detail & Related papers  (2023-10-12T11:25:46Z)
• Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text [5.532477732693001]
  We show that a large language model can serve as a highly effective few-shot semantically. It can convert natural language sentences into a logical form that serves as input for answer set programs. We demonstrate that this method achieves state-of-the-art performance on several benchmarks, including bAbI, StepGame, CLUTRR, and gSCAN.
  arXiv  Detail & Related papers  (2023-07-15T03:29:59Z)
• Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model [63.66204449776262]
  Instruct2Act is a framework that maps multi-modal instructions to sequential actions for robotic manipulation tasks. Our approach is adjustable and flexible in accommodating various instruction modalities and input types. Our zero-shot method outperformed many state-of-the-art learning-based policies in several tasks.
  arXiv  Detail & Related papers  (2023-05-18T17:59:49Z)
• PaLM-E: An Embodied Multimodal Language Model [101.29116156731762]
  We propose embodied language models to incorporate real-world continuous sensor modalities into language models. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks. Our largest model, PaLM-E-562B with 562B parameters, is a visual-language generalist with state-of-the-art performance on OK-VQA.
  arXiv  Detail & Related papers  (2023-03-06T18:58:06Z)
• Grounding Language with Visual Affordances over Unstructured Data [26.92329260907805]
  We propose a novel approach to efficiently learn language-conditioned robot skills from unstructured, offline and reset-free data. We exploit a self-supervised visuo-lingual affordance model, which requires as little as 1% of the total data with language. We find that our method is capable of completing long-horizon, multi-tier tasks in the real world, while requiring an order of magnitude less data than previous approaches.
  arXiv  Detail & Related papers  (2022-10-04T21:16:48Z)
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances [119.29555551279155]
  Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions.
  arXiv  Detail & Related papers  (2022-04-04T17:57:11Z)
• Pre-Trained Language Models for Interactive Decision-Making [72.77825666035203]
  We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings. We demonstrate that this framework enables effective generalization across different environments. For test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6%.
  arXiv  Detail & Related papers  (2022-02-03T18:55:52Z)
• 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)
• Pre-training Universal Language Representation [46.51685959045527]
  This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. We empirically verify that well designed pre-training scheme may effectively yield universal language representation.
  arXiv  Detail & Related papers  (2021-05-30T09:29:01Z)
• Language Conditioned Imitation Learning over Unstructured Data [9.69886122332044]
  We present a method for incorporating free-form natural language conditioning into imitation learning. Our approach learns perception from pixels, natural language understanding, and multitask continuous control end-to-end as a single neural network. We show this dramatically improves language conditioned performance, while reducing the cost of language annotation to less than 1% of total data.
  arXiv  Detail & Related papers  (2020-05-15T17:08:50Z)

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.