Toward cloud-based grasping with uncertainty in shape: Estimating lower bounds on achieving force closure with zero-slip push grasps

Kehoe, Ben; Berenson, Dmitry; Goldberg, Ken

doi:10.1109/icra.2012.6224781

Cited by 46 publications

(43 citation statements)

References 31 publications

(28 reference statements)

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“…The rapid progress of wireless technologies and the availability of commercial data centers, with high-bandwidth connections and highly scalable computation, storage, and communication infrastructures ('the cloud' [1]) may allow robots to overcome many of the current bottlenecks. Currently, several frameworks [2], [3], [4], [5] and robotic applications [6], [7] are being developed to exploit the cloud's potential for creating light, fast, and intelligent low-cost robots.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots

Mohanarajah

Usenko

Singh³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

121

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Abstract-This paper presents an architecture, protocol, and parallel algorithms for collaborative 3D mapping in the cloud with low-cost robots. The robots run a dense visual odometry algorithm on a smartphone-class processor. Key-frames from the visual odometry are sent to the cloud for parallel optimization and merging with maps produced by other robots. After optimization the cloud pushes the updated poses of the local key-frames back to the robots. All processes are managed by Rapyuta, a cloud robotics framework that runs in a commercial data center. The paper includes qualitative visualization of collaboratively built maps, as well as quantitative evaluation of localization accuracy, bandwidth usage, processing speeds, and map storage.Note to Practitioners-This paper presents an architecture for cloud-based collaborative 3D mapping with low-cost robots. The low-cost robots used in this work consist mainly of a mobile base, a smart phone class processor, an RGB-D sensor and a wireless interface. Each robot runs its own visual odometry algorithm, which estimates the pose of the robot using the color and the depth frames (images) from the RGB-D sensor. The dense visual odometry algorithm presented herein uses no image features and requires no specialized hardware. In addition to pose estimation, the visual odometry algorithm also produces key-frames, which is a subset of frames that in a way summarizes the motion of the robot. These key-frames are sent to the cloud for further optimization and merging with the key-frames produced by other robots. By sending only the key-frames (instead of all the frames produced by the sensor), bandwidth requirements are significantly reduced. Each robot is connected to the cloud infrastructure using a WebSocket-based bidirectional full duplex communication channel. The cloud infrastructure is provided using Rapyuta, a Platform-as-a-Service framework for building scalable cloud robotics applications. The key-frame pose optimization and the merging processes are parallelized in order to make them scalable. The updated key-frame poses are eventually sent back to the robot to improve its localization accuracy. In addition to describing the architecture and the design choices, the paper provides qualitative and quantitative evaluations of the integrated system.

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Section: Introductionmentioning

confidence: 99%

Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots

Mohanarajah

Usenko

Singh³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

121

View full text Add to dashboard Cite

show abstract

“…To cope with uncertainty in grasping, most previous works using sensory feedback focused on uncertainties originating from imprecise model of object's geometry [3], [4], object position and orientation [5], [6], [7], [8]. These geometric uncertainties can directly influence the relative configuration between the robotic hand and the object, upon which the grasp stability is built [9].…”

Section: Introductionmentioning

confidence: 99%

Learning of grasp adaptation through experience and tactile sensing

Bekiroglu

Kragić

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-To perform robust grasping, a multi-fingered robotic hand should be able to adapt its grasping configuration, i.e., how the object is grasped, to maintain the stability of the grasp. Such a change of grasp configuration is called grasp adaptation and it depends on the controller, the employed sensory feedback and the type of uncertainties inherit to the problem. This paper proposes a grasp adaptation strategy to deal with uncertainties about physical properties of objects, such as the object weight and the friction at the contact points. Based on an object-level impedance controller, a grasp stability estimator is first learned in the object frame. Once a grasp is predicted to be unstable by the stability estimator, a grasp adaptation strategy is triggered according to the similarity between the new grasp and the training examples. Experimental results demonstrate that our method improves the grasping performance on novel objects with different physical properties from those used for training.

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“…Other tasks that can benefit from the cloud and that are closer to manufacturing include grasp planning [2] and object recognition [36]. Both tasks are computationally expensive, highly parallelizable, and require a significant amount of storage.…”

Section: B Demonstration: Dense Mapping With Rapyutamentioning

confidence: 99%

“…The popular PaaS framework Heroku [7] overcomes some of these limitations, but lacks features required for many robotics applications, such as bidirectional data flow between robots and their computing environments. Other, more recent PaaS frameworks such as Cloud Foundry [8] and OpenShift [9] are relatively liberal in terms of available runtimes and languages, but typically expect applications to be single processes or preconfigured set of parent and child processes 2 running inside a computing environment that has only an HTTP connection to the outside 3 . However, when it comes to robotics applications, such as the ones based on ROS, processes (ROS nodes) typically run as a computation graph and are dynamically configured to provide services for robots.…”

Section: Introductionmentioning

confidence: 99%

Rapyuta: A Cloud Robotics Platform

Mohanarajah

Hunziker

D’Andrea

et al. 2015

IEEE Trans. Automat. Sci. Eng.

230

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Abstract-In this paper we present the design and implementation of Rapyuta1 , an open source cloud robotics platform. Rapyuta helps robots to offload heavy computation by providing secured customizable computing environments in the cloud. The computing environments also allow the robots to easily access the RoboEarth knowledge repository. Furthermore, these computing environments are tightly interconnected, paving the way for deployment of robotic teams. We also describe three typical use cases, some benchmarking and performance results, and two proof-of-concept demonstrations.Note to Practitioners-Rapyuta allows to outsource some or all of a robot's onboard computational processes to a commercial data center. Its main difference to other, similar frameworks like the Google App Engine is that it is specifically tailored towards multi-process high-bandwidth robotics applications/middlewares and provides a well documented open source implementation that can be modified to cover a large variety of robotic scenarios. Rapyuta supports the outsourcing of almost all of the current 3000+ ROS packages out of the box and is easily extensible to other robotic middleware. A pre-installed Amazon Machine Image (AMI) is provided that allows to launch Rapyuta in any of Amazon's data center within minutes. Once launched, robots can authenticate themselves to Rapyuta, create one or more secured computational environments in the cloud, and launch the desired nodes/processes. The computing environments can also be arbitrarily connected to build parallel computing architectures on the fly. The WebSocket-based communication protocol, which provides synchronous and asynchronous communication mechanisms, allows not only ROS based robots, but also browsers and mobiles phones to connect to the ecosystem. Rapyuta's computing environments are private, secure, and optimized for data throughput. However, its performance is in large part determined by the latency and quality of the network connection and the performance of the data center. Optimizing performance under these constraints is typically highly application specific. The paper illustrates an example of performance optimization in a collaborative real-time 3D mapping application. Other target applications include collaborative 3D mapping, task/grasp planning, object recognition, localization, and teleoperation, among others.

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Toward cloud-based grasping with uncertainty in shape: Estimating lower bounds on achieving force closure with zero-slip push grasps

Cited by 46 publications

References 31 publications

Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots

Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots

Learning of grasp adaptation through experience and tactile sensing

Rapyuta: A Cloud Robotics Platform

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