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Efficiently Aligning Robots with Human Preferences: A New Learning Method

Pre-trained visuomotor robot policies, trained on massive datasets, are revolutionizing robotics. However, aligning these policies with human preferences remains a significant hurdle, especially when those preferences are difficult to articulate explicitly. A new paper, “Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment,” introduces a novel method called Representation-Aligned Preference-based Learning (RAPL) that tackles this problem.

Traditional reinforcement learning from human feedback (RLHF) has proven effective for non-embodied AI like large language models. However, RLHF is impractical for robots. Training a robot to reliably follow human preferences often requires an unrealistic amount of human feedback—a single task might take a full day to collect the necessary preference data.

RAPL offers a more efficient solution. Instead of directly learning a reward function from many human preferences, RAPL leverages a two-step approach:

  1. Visual Representation Alignment: RAPL focuses human feedback on fine-tuning a pre-trained vision encoder. The encoder is a neural network that processes images and extracts relevant features. The goal is to align the robot’s representation of the world with the user’s visual representation. For instance, if a user prefers that a robot grasp a bag of chips by its edges rather than squeezing the bag, RAPL would learn to extract visual features that highlight these preferred grasp locations. This alignment is achieved through a preference-based metric learning process using triplet comparisons of image sequences provided by the user. The user is shown three sequences and ranks them based on their preference.

  2. Reward Construction via Feature Matching: Once the robot’s vision encoder is aligned with the user’s visual representation, RAPL constructs a dense visual reward function using optimal transport (OT). OT is a mathematical technique that finds the most efficient way to “match” the robot’s feature distribution to the user’s preferred feature distribution. This means the robot is rewarded for creating visual features similar to those exhibited in the preferred user demonstrations.

The researchers demonstrated RAPL’s effectiveness through both simulated and real-world experiments. In simulations, RAPL learned visual rewards with significantly less human feedback than RLHF, while showing strong generalization across different robot embodiments. Specifically, RAPL achieved comparable performance to RLHF while using only 20% of the human data.

Real-world experiments involved three challenging object manipulation tasks: picking up a bag of chips, a cup, and a fork. Here, RAPL drastically reduced the amount of real human feedback required, fine-tuning pre-trained policies with 5x less human data than traditional RLHF. Visual examples showed the robot shifting from inefficient and potentially damaging grasps (e.g., crushing chips) to the preferred behaviors identified by the human feedback.

RAPL offers a promising path towards building more human-friendly robots. Its efficient use of human feedback could significantly accelerate the development and deployment of robots capable of reliably performing tasks in accordance with human preferences. Future research could explore the scalability and robustness of RAPL across a wider range of tasks and robot platforms.