Task Success Prediction for Open-Vocabulary Manipulation Based on Multi-Level Aligned Representations

• URL: http://arxiv.org/abs/2410.00436v1
• Date: Tue, 1 Oct 2024 06:35:34 GMT
• Title: Task Success Prediction for Open-Vocabulary Manipulation Based on Multi-Level Aligned Representations
• Authors: Miyu Goko, Motonari Kambara, Daichi Saito, Seitaro Otsuki, Komei Sugiura,
• Abstract summary: We propose Contrastive $lambda$-Repformer, which predicts task success for table-top manipulation tasks by aligning images with instruction sentences. Our method integrates the following three key types of features into a multi-level aligned representation. We evaluate Contrastive $lambda$-Repformer on a dataset based on a large-scale standard dataset, the RT-1 dataset, and on a physical robot platform.
• Score: 1.1650821883155187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we consider the problem of predicting task success for open-vocabulary manipulation by a manipulator, based on instruction sentences and egocentric images before and after manipulation. Conventional approaches, including multimodal large language models (MLLMs), often fail to appropriately understand detailed characteristics of objects and/or subtle changes in the position of objects. We propose Contrastive $\lambda$-Repformer, which predicts task success for table-top manipulation tasks by aligning images with instruction sentences. Our method integrates the following three key types of features into a multi-level aligned representation: features that preserve local image information; features aligned with natural language; and features structured through natural language. This allows the model to focus on important changes by looking at the differences in the representation between two images. We evaluate Contrastive $\lambda$-Repformer on a dataset based on a large-scale standard dataset, the RT-1 dataset, and on a physical robot platform. The results show that our approach outperformed existing approaches including MLLMs. Our best model achieved an improvement of 8.66 points in accuracy compared to the representative MLLM-based model.

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