In appendix, we Reward OptimizationWepropose .

We additionally assume that the agent cannot interact with the environment but has access to the action-labeled transition data collected by some agents with unknown qualities.

Inthissection compare compare learned theprior environment 6. Experimental Tostudy proposed [54], where froman learning.

Note f = , so havethat / f =1.

Inthiswork,wepresentBAKU,asimple 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 tosubstantially improveupon prior work.

Intelligent agents must be generalists, capable of quickly adapting to various tasks.

However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities.