Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks

• URL: http://arxiv.org/abs/2403.17238v1
• Date: Mon, 25 Mar 2024 22:39:20 GMT
• Title: Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks
• Authors: Jonathan Salfity, Selma Wanna, Minkyu Choi, Mitch Pryor,
• Abstract summary: We present an automated framework to decompose trajectory data into temporally bounded and natural language-based descriptive sub-tasks. Our framework provides both time-based and language-based descriptions for lower-level sub-tasks that comprise full trajectories. The metrics measure the temporal alignment and semantic fidelity of language descriptions between two sub-task decompositions.
• Score: 1.8124328823188356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works in Task and Motion Planning (TAMP) show that training control policies on language-supervised robot trajectories with quality labeled data markedly improves agent task success rates. However, the scarcity of such data presents a significant hurdle to extending these methods to general use cases. To address this concern, we present an automated framework to decompose trajectory data into temporally bounded and natural language-based descriptive sub-tasks by leveraging recent prompting strategies for Foundation Models (FMs) including both Large Language Models (LLMs) and Vision Language Models (VLMs). Our framework provides both time-based and language-based descriptions for lower-level sub-tasks that comprise full trajectories. To rigorously evaluate the quality of our automatic labeling framework, we contribute an algorithm SIMILARITY to produce two novel metrics, temporal similarity and semantic similarity. The metrics measure the temporal alignment and semantic fidelity of language descriptions between two sub-task decompositions, namely an FM sub-task decomposition prediction and a ground-truth sub-task decomposition. We present scores for temporal similarity and semantic similarity above 90%, compared to 30% of a randomized baseline, for multiple robotic environments, demonstrating the effectiveness of our proposed framework. Our results enable building diverse, large-scale, language-supervised datasets for improved robotic TAMP.

Related papers

• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER [26.008350261239617]
  We propose a Multi-Task Semantic Decomposition Framework via Joint Task-specific Pre-training for few-shot NER. We introduce two novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM) and Class Contrastive Discrimination. In the downstream main task, we introduce a multi-task joint optimization framework with the semantic decomposing method, which facilitates the model to integrate two different semantic information for entity classification.
  arXiv  Detail & Related papers  (2023-08-28T12:46:21Z)
• Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations [22.894541507068933]
  This paper presents our approach to build generalized models for the Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations Challenge of DSTC-10. We employ extensive data augmentation strategies on written data, including artificial error injection and round-trip text-speech transformation. Our approach ranks third on the objective evaluation and second on the final official human evaluation.
  arXiv  Detail & Related papers  (2022-03-08T12:26:57Z)
• Incorporating Linguistic Knowledge for Abstractive Multi-document Summarization [20.572283625521784]
  We develop a neural network based abstractive multi-document summarization (MDS) model. We process the dependency information into the linguistic-guided attention mechanism. With the help of linguistic signals, sentence-level relations can be correctly captured.
  arXiv  Detail & Related papers  (2021-09-23T08:13:35Z)
• Exploring Relational Context for Multi-Task Dense Prediction [76.86090370115]
  We consider a multi-task environment for dense prediction tasks, represented by a common backbone and independent task-specific heads. We explore various attention-based contexts, such as global and local, in the multi-task setting. We propose an Adaptive Task-Relational Context module, which samples the pool of all available contexts for each task pair.
  arXiv  Detail & Related papers  (2021-04-28T16:45:56Z)
• Structured Prediction as Translation between Augmented Natural Languages [109.50236248762877]
  We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks. Instead of tackling the problem by training task-specific discriminatives, we frame it as a translation task between augmented natural languages. Our approach can match or outperform task-specific models on all tasks, and in particular, achieves new state-of-the-art results on joint entity and relation extraction.
  arXiv  Detail & Related papers  (2021-01-14T18:32:21Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)

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.