Fleets of robots ingest massive amounts of streaming data generated by
interacting with their environments, far more than those that can be stored or
transmitted with ease. At the same time, we hope that teams of robots can
co-acquire diverse skills through their experiences in varied settings. How can
we enable such fleet-level learning without having to transmit or centralize
fleet-scale data? In this paper, we investigate distributed learning of
policies as a potential solution. To efficiently merge policies in the
distributed setting, we propose fleet-merge, an instantiation of distributed
learning that accounts for the symmetries that can arise in learning policies
that are parameterized by recurrent neural networks. We show that fleet-merge
consolidates the behavior of policies trained on 50 tasks in the Meta-World
environment, with the merged policy achieving good performance on nearly all
training tasks at test time. Moreover, we introduce a novel robotic tool-use
benchmark, fleet-tools, for fleet policy learning in compositional and
contact-rich robot manipulation tasks, which might be of broader interest, and
validate the efficacy of fleet-merge on the benchmark.

研究通过分布式学习实现机器人舰队在各种环境中获得多样化技能的方法，并介绍了 fleet-merge 方法用于合并参数化的递归神经网络策略，在 Meta-World 环境中有效地合并 50 个任务的策略，并在机器人工具使用基准测试中验证了 fleet-merge 的功效。