Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control

• URL: http://arxiv.org/abs/2405.05852v1
• Date: Thu, 9 May 2024 15:39:54 GMT
• Title: Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
• Authors: Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner,
• Abstract summary: Embodied AI agents require a fine-grained understanding of the physical world mediated through visual and language inputs. We consider pre-trained text-to-image diffusion models, which are explicitly optimized to generate images from text prompts. We show that Stable Control Representations enable learning policies that exhibit state-of-the-art performance on OVMM, a difficult open-vocabulary navigation benchmark.
• Score: 73.6361029556484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied AI agents require a fine-grained understanding of the physical world mediated through visual and language inputs. Such capabilities are difficult to learn solely from task-specific data. This has led to the emergence of pre-trained vision-language models as a tool for transferring representations learned from internet-scale data to downstream tasks and new domains. However, commonly used contrastively trained representations such as in CLIP have been shown to fail at enabling embodied agents to gain a sufficiently fine-grained scene understanding -- a capability vital for control. To address this shortcoming, we consider representations from pre-trained text-to-image diffusion models, which are explicitly optimized to generate images from text prompts and as such, contain text-conditioned representations that reflect highly fine-grained visuo-spatial information. Using pre-trained text-to-image diffusion models, we construct Stable Control Representations which allow learning downstream control policies that generalize to complex, open-ended environments. We show that policies learned using Stable Control Representations are competitive with state-of-the-art representation learning approaches across a broad range of simulated control settings, encompassing challenging manipulation and navigation tasks. Most notably, we show that Stable Control Representations enable learning policies that exhibit state-of-the-art performance on OVMM, a difficult open-vocabulary navigation benchmark.

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