ROSO: Improving Robotic Policy Inference via Synthetic Observations

• URL: http://arxiv.org/abs/2311.16680v2
• Date: Wed, 29 Nov 2023 05:16:40 GMT
• Title: ROSO: Improving Robotic Policy Inference via Synthetic Observations
• Authors: Yusuke Miyashita, Dimitris Gahtidis, Colin La, Jeremy Rabinowicz, Jurgen Leitner
• Abstract summary: We propose the use of generative artificial intelligence to improve zero-shot performance of a pre-trained policy. Our experiments show that incorporating generative AI into robotic inference significantly improves successful outcomes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose the use of generative artificial intelligence (AI) to improve zero-shot performance of a pre-trained policy by altering observations during inference. Modern robotic systems, powered by advanced neural networks, have demonstrated remarkable capabilities on pre-trained tasks. However, generalizing and adapting to new objects and environments is challenging, and fine-tuning visuomotor policies is time-consuming. To overcome these issues we propose Robotic Policy Inference via Synthetic Observations (ROSO). ROSO uses stable diffusion to pre-process a robot's observation of novel objects during inference time to fit within its distribution of observations of the pre-trained policies. This novel paradigm allows us to transfer learned knowledge from known tasks to previously unseen scenarios, enhancing the robot's adaptability without requiring lengthy fine-tuning. Our experiments show that incorporating generative AI into robotic inference significantly improves successful outcomes, finishing up to 57% of tasks otherwise unsuccessful with the pre-trained policy.

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