BAKU: マルチタスク方策学習のための効率的Transformer
BAKU: An Efficient Transformer for Multi-Task Policy Learning
観察トランク・行動チャンキング・マルチセンサ観察・行動ヘッドを組み合わせたTransformerで、少ない実演データから多様なロボットタスクの方策を効率的に学習する。
著者: Siddhant Haldar, Zhuoran Peng, Lerrel Pinto
分類: cs.RO
原文アブストラクト
Training generalist agents capable of solving diverse tasks is challenging, often requiring large datasets of expert demonstrations. This is particularly problematic in robotics, where each data point requires physical execution of actions in the real world. Thus, there is a pressing need for architectures that can effectively leverage the available training data. In this work, we present BAKU, a simple transformer architecture that enables efficient learning of multi-task robot policies. BAKU builds upon recent advancements in offline imitation learning and meticulously combines observation trunks, action chunking, multi-sensory observations, and action heads to substantially improve upon prior work. Our experiments on 129 simulated tasks across LIBERO, Meta-World suite, and the Deepmind Control suite exhibit an overall 18% absolute improvement over RT-1 and MT-ACT, with a 36% improvement on the harder LIBERO benchmark. On 30 real-world manipulation tasks, given an average of just 17 demonstrations per task, BAKU achieves a 91% success rate. Videos of the robot are best viewed at https://baku-robot.github.io/.
