ROSO: Improving Robotic Policy Inference via Synthetic Observations (2311.16680v2)

Abstract: In this paper, we propose the use of generative 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.

• The paper introduces ROSO, which leverages generative AI to modify instructions and images, enabling robots to handle unseen objects without extensive retraining.
• The methodology employs color mapping and image editing via Stable Diffusion to convert unfamiliar observations into recognized formats, achieving up to a 57% success rate increase.
• The study emphasizes that high-quality image edits and consistent object generation are crucial for aligning generative outputs with pre-trained models, improving overall task performance.

Objectives and Challenges in Robotic Policy Inference

Robotic systems have progressed significantly with neural network integration, enhancing their ability to perform complex tasks, such as pick-and-place operations. However, deploying these systems in unfamiliar environments or with unseen objects is a hurdle due to the high computational cost involved in retraining or fine-tuning on new data.

Introducing ROSO

An innovative approach has been put forward called Robotic Policy Inference via Synthetic Observations (ROSO). The concept involves the use of generative AI to alter a robot's sensory data during policy execution to fit within the distribution of pre-trained tasks. This is achieved by pre-processing novel observations using a model called Stable Diffusion.

Methodology

ROSO consists of two key parts: instruction modification and image modification. For instruction modification, unseen object colors are mapped to seen ones using a colormap of previously successful tasks. Within image modification, tasks are performed with a focus on semantic meaning and quality of image edits, using generative models to replace unseen objects with seen equivalents. An example would be altering a blue cube (unseen during training) to a red cube (seen) to fool the pre-trained networks into recognizing the object.

Results and Observations

The paper's experiments show that incorporating generative AI significantly enhances task performance, with sizable success rate increases, particularly in scenarios involving unseen object colors, objects, or backgrounds. For example, unseen background color tasks saw a 57% increase in successful outcomes.

Challenges and Considerations

Despite the success, failures in the ROSO pipeline were noted, mainly due to object detection inaccuracies and image quality issues during object transformation. The paper shows that object modification based on image edit quality typically yields better results than simply using semantic meaning. Lastly, consistent object generation and the alignment of generative models' output with the training data are essential for robust performance. The pipeline improvement could lead to a more sophisticated understanding of integrating perceived environments with robotic action without extensive retraining.