We introduce OpenBot-Fleet, a comprehensive open-source cloud robotics system
for navigation. OpenBot-Fleet uses smartphones for sensing, local compute and
communication, Google Firebase for secure cloud storage and off-board compute,
and a robust yet low-cost wheeled robot toact in real-world environments. The
robots collect task data and upload it to the cloud where navigation policies
can be learned either offline or online and can then be sent back to the robot
fleet. In our experiments we distribute 72 robots to a crowd of workers who
operate them in homes, and show that OpenBot-Fleet can learn robust navigation
policies that generalize to unseen homes with >80% success rate. OpenBot-Fleet
represents a significant step forward in cloud robotics, making it possible to
deploy large continually learning robot fleets in a cost-effective and scalable
manner. All materials can be found at this https URL A video is
available at this https URL

OpenBot-Fleet 是一个开源的云机器人系统，利用智能手机感知、本地计算和通信，Google Firebase 进行安全的云存储和离线计算，以及强大且低成本的轮式机器人在真实环境中执行任务，并在云中学习导航策略，实验表明 OpenBot-Fleet 可在各种未知环境下成功导航，是云机器人领域的重要进展。

OpenBot-Fleet: 实际机器人的集体学习系统

OpenBot-Fleet: A System for Collective Learning with Real Robots

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。

基于权重合并的船队策略学习及其在机器人工具使用中的应用

Fleet Policy Learning via Weight Merging and An Application to Robotic  Tool-Use

Commercial and industrial deployments of robot fleets at Amazon, Nimble, Plus
One, Waymo, and Zoox query remote human teleoperators when robots are at risk
or unable to make task progress. With continual learning, interventions from
the remote pool of humans can also be used to improve the robot fleet control
policy over time. A central question is how to effectively allocate limited
human attention. Prior work addresses this in the single-robot, single-human
setting; we formalize the Interactive Fleet Learning (IFL) setting, in which
multiple robots interactively query and learn from multiple human supervisors.
We propose Return on Human Effort (ROHE) as a new metric and Fleet-DAgger, a
family of IFL algorithms. We present an open-source IFL benchmark suite of
GPU-accelerated Isaac Gym environments for standardized evaluation and
development of IFL algorithms. We compare a novel Fleet-DAgger algorithm to 4
baselines with 100 robots in simulation. We also perform a physical
block-pushing experiment with 4 ABB YuMi robot arms and 2 remote humans.
Experiments suggest that the allocation of humans to robots significantly
affects the performance of the fleet, and that the novel Fleet-DAgger algorithm
can achieve up to 8.8x higher ROHE than baselines. See
this https URL for supplemental material.

研究了交互式机群学习 (IFL) 中有限人力资源的有效分配问题，提出了一种新的度量 ROHE 以及一种 IFL 算法家族 Fleet-DAgger，并通过实验验证了该算法可以在人力资源有限的情况下实现更高的 ROHE。