Fine-Tuning Robot Policies While Maintaining User Privacy

Abstract

Recent works introduce general-purpose robot policies. These policies provide a strong prior over how robots should behave -- e.g., how a robot arm should manipulate food items. But in order for robots to match an individual person's needs, users typically fine-tune these generalized policies -- e.g., showing the robot arm how to make their own preferred dinners. Importantly, during the process of personalizing robots, end-users leak data about their preferences, habits, and styles (e.g., the foods they prefer to eat). Other agents can simply roll-out the fine-tuned policy and see these personally-trained behaviors. This leads to a fundamental challenge: how can we develop robots that personalize actions while keeping learning private from external agents? We here explore this emerging topic in human-robot interaction and develop PRoP, a model-agnostic framework for personalized and private robot policies. Our core idea is to equip each user with a unique key; this key is then used to mathematically transform the weights of the robot's network. With the correct key, the robot's policy switches to match that user's preferences -- but with incorrect keys, the robot reverts to its baseline behaviors. We show the general applicability of our method across multiple model types in imitation learning, reinforcement learning, and classification tasks. PRoP is practically advantageous because it retains the architecture and behaviors of the original policy, and experimentally outperforms existing encoder-based approaches.