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Title: Mitigating Network Latency in Cloud-Based Teleoperation using Motion Segmentation and Synthesis
Network latency is a major problem in Cloud Robotics for human robot interactions such as teleoperation. Routing delays can be highly variable in a heterogeneous computing environment, imposing challenges to reliably teleoperate a robot with a closed-loop feedback controller. By sharing Gaussian Mixture Models (GMMs), Hidden Semi- Markov Models (HSMMs), and linear quadratic tracking (LQT) con- trollers between the cloud and the robot. We build a motion recognition, segmentation, and synthesis framework for Cloud Robotic teleoperation; and we introduce a set of latency mitigation network protocols under this framework. We use this framework in experiments with a dynamic robot arm to perform learned hand-written letter motions.We then study the motion recognition errors, motion synthesis errors, and the latency mitigation performance.
Authors:
; ; ;
Award ID(s):
1838833
Publication Date:
NSF-PAR ID:
10204349
Journal Name:
International Symposium on Robotics Research
Sponsoring Org:
National Science Foundation
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