Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards
  for Visuomotor Robot Policy Alignment
Authors: name: Ran Tian, name: Yilin Wu, name: Chenfeng Xu, name: Masayoshi Tomizuka, name: Jitendra Malik, name: Andrea Bajcsy

Visuomotor robot policies, increasingly pre-trained on large-scale datasets,
promise significant advancements across robotics domains. However, aligning
these policies with end-user preferences remains a challenge, particularly when
the preferences are hard to specify. While reinforcement learning from human
feedback (RLHF) has become the predominant mechanism for alignment in
non-embodied domains like large language models, it has not seen the same
success in aligning visuomotor policies due to the prohibitive amount of human
feedback required to learn visual reward functions. To address this limitation,
we propose Representation-Aligned Preference-based Learning (RAPL), an
observation-only method for learning visual rewards from significantly less
human preference feedback. Unlike traditional RLHF, RAPL focuses human feedback
on fine-tuning pre-trained vision encoders to align with the end-user's visual
representation and then constructs a dense visual reward via feature matching
in this aligned representation space. We first validate RAPL through simulation
experiments in the X-Magical benchmark and Franka Panda robotic manipulation,
demonstrating that it can learn rewards aligned with human preferences, more
efficiently uses preference data, and generalizes across robot embodiments.
Finally, our hardware experiments align pre-trained Diffusion Policies for
three object manipulation tasks. We find that RAPL can fine-tune these policies
with 5x less real human preference data, taking the first step towards
minimizing human feedback while maximizing visuomotor robot policy alignment.