A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

Sim2Real View Invariant Visual Servoing by Recurrent Control

Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints and angles, even in the presence of optical distortions. In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback. In this paper, we study how viewpoint-invariant visual servoing skills can be learned automatically in a robotic manipulation scenario. To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object. The problem that must be solved by this controller is fundamentally ambiguous: under severe variation in viewpoint, it may be impossible to determine the actions in a single feedforward operation. Instead, our visual servoing system must use its memory of past movements to understand how the actions affect the robot motion from the current viewpoint, correcting mistakes and gradually moving closer to the target. This ability is in stark contrast to most visual servoing methods, which either assume known dynamics or require a calibration phase. We show how we can learn this recurrent controller using simulated data and a reinforcement learning objective. We then describe how the resulting model can be transferred to a real-world robot by disentangling perception from control and only adapting the visual layers. The adapted model can servo to previously unseen objects from novel viewpoints on a real-world Kuka IIWA robotic arm. For supplementary videos, see: https://fsadeghi.github.io/Sim2RealViewInvariantServoComment: Supplementary video: https://fsadeghi.github.io/Sim2RealViewInvariantServ

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch through model-free deep reinforcement learning. Our method involves training two fully convolutional networks that map from visual observations to actions: one infers the utility of pushes for a dense pixel-wise sampling of end effector orientations and locations, while the other does the same for grasping. Both networks are trained jointly in a Q-learning framework and are entirely self-supervised by trial and error, where rewards are provided from successful grasps. In this way, our policy learns pushing motions that enable future grasps, while learning grasps that can leverage past pushes. During picking experiments in both simulation and real-world scenarios, we find that our system quickly learns complex behaviors amid challenging cases of clutter, and achieves better grasping success rates and picking efficiencies than baseline alternatives after only a few hours of training. We further demonstrate that our method is capable of generalizing to novel objects. Qualitative results (videos), code, pre-trained models, and simulation environments are available at http://vpg.cs.princeton.eduComment: To appear at the International Conference On Intelligent Robots and Systems (IROS) 2018. Project webpage: http://vpg.cs.princeton.edu Summary video: https://youtu.be/-OkyX7Zlhi