26,810 research outputs found
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
Social navigation is the capability of an autonomous agent, such as a robot,
to navigate in a 'socially compliant' manner in the presence of other
intelligent agents such as humans. With the emergence of autonomously
navigating mobile robots in human populated environments (e.g., domestic
service robots in homes and restaurants and food delivery robots on public
sidewalks), incorporating socially compliant navigation behaviors on these
robots becomes critical to ensuring safe and comfortable human robot
coexistence. To address this challenge, imitation learning is a promising
framework, since it is easier for humans to demonstrate the task of social
navigation rather than to formulate reward functions that accurately capture
the complex multi objective setting of social navigation. The use of imitation
learning and inverse reinforcement learning to social navigation for mobile
robots, however, is currently hindered by a lack of large scale datasets that
capture socially compliant robot navigation demonstrations in the wild. To fill
this gap, we introduce Socially CompliAnt Navigation Dataset (SCAND) a large
scale, first person view dataset of socially compliant navigation
demonstrations. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of
socially compliant, human teleoperated driving demonstrations that comprises
multi modal data streams including 3D lidar, joystick commands, odometry,
visual and inertial information, collected on two morphologically different
mobile robots a Boston Dynamics Spot and a Clearpath Jackal by four different
human demonstrators in both indoor and outdoor environments. We additionally
perform preliminary analysis and validation through real world robot
experiments and show that navigation policies learned by imitation learning on
SCAND generate socially compliant behavior
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
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