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Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power on demand, but tapping into that power from a robot is non-trivial. We present FogROS2, an open-source platform to facilitate cloud and fog robotics that is compatible with the emerging Robot Operating System 2 (ROS 2) standard. FogROS2 is completely redesigned and distinct from its predecessor FogROS1 in 9 ways, and has lower latency, overhead, and startup times; improved usability, and additional automa-tion, such as region and computer type selection. Additionally, FogROS2 was added to the official distribution of ROS 2, gaining performance, timing, and additional improvements associated with ROS 2. In examples, FogROS2 reduces SLAM latency by 50 %, reduces grasp planning time from 14 s to 1.2 s, and speeds up motion planning 28x. When compared to FogROS1, FogROS2 reduces network utilization by up to 3.8x, improves startup time by 63 %, and network round-trip latency by 97 %for images using video compression. The source code, examples, and documentation for FogROS2 are available at https://github.com/BerkeleyAutomation/FogROS2, and is available through the official ROS 2 repository at https://index.ros.org/p/fogros2/ 

As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power onboard. Despite its immense computing capacity, it is often underused by the robotics and automation community due to lack of expertise in cloud computing and cloud-based infrastructure. Fog Robotics balances computing and data between cloud edge devices. We propose a software framework, FogROS, as an extension of the Robot Operating System (ROS), the de-facto standard for creating robot automation applications and components. It allows researchers to deploy components of their software to the cloud with minimal effort, and correspondingly gain access to additional computing cores, GPUs, FPGAs, and TPUs, as well as predeployed software made available by other researchers. FogROS allows a researcher to specify which components of their software will be deployed to the cloud and to what type of computing hardware. We evaluate FogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2), (2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-core motion planning using a 96-core cloud-based server. In all three examples, a component is deployed to the cloud and accelerated with a small change in system launch configuration, while incurring additional latency of 1.2 s, 0.6 s, and 0.5 s due to network communication, the computation speed is improved by 2.6x, 6.0x and 34.2x, respectively. 

Mechanical search, the finding and extracting of a known target object from a cluttered environment, is a key challenge in automating warehouse, home, retail, and industrial tasks. In this paper, we consider contexts in which occluding objects are to remain untouched, thus minimizing disruptions and avoiding toppling. We assume a 6-DOF robot with an RGBD camera and unicontact suction gripper mounted on its wrist. With this setup, the robot can move both camera and gripper in order to identify a suitable approach vector, reach in to achieve a suction grasp of the target object, and extract it. We present AVPLUG: Approach Vector PLanning for Unicontact Grasping, an algorithm that uses an octree occupancy model and Minkowski sum computation to find a collision-free grasp approach vector. Experiments in simulation and with a physical Fetch robot suggest that AVPLUG finds an approach vector up to 20× faster than a baseline search policy. 

The difficulty in specifying rewards for many real world problems has led to an increased focus on learning rewards from human feedback, such as demonstrations. However, there are often many different reward functions that explain the human feedback, leaving agents with uncertainty over what the true reward function is. While most policy optimization approaches handle this uncertainty by optimizing for expected performance, many applications demand risk-averse behavior. We derive a novel policy gradient-style robust optimization approach, PG-BROIL, that optimizes a soft-robust objective that balances expected performance and risk. To the best of our knowledge, PG-BROIL is the first policy optimization algorithm robust to a distribution of reward hypotheses which can scale to continuous MDPs. Results suggest that PG-BROIL can produce a family of behaviors ranging from risk-neutral to risk-averse and outperforms state-of-the-art imitation learning algorithms when learning from ambiguous demonstrations by hedging against uncertainty, rather than seeking to uniquely identify the demonstrator’s reward function.