Topological Methods for Motion Prediction and Caging

Time: Tue 2020-03-17 10.00

Location: F3, Lindstedtsvägen 26, 114 28 Stockholm (English)

Subject area: Computer Science

Doctoral student: Joao Frederico Pinto Basto de Carvalho , Robotik, perception och lärande, RPL, Centrum för autonoma system, CAS

Opponent: Associate Professor Frank van der Stappen, Utrecht University, Utrecht, the Netherlands

Supervisor: Danica Kragic, Centrum för autonoma system, CAS

Abstract

To fulfill the requirements of automation in unstructured environmentsit will be necessary to endow robots with the ability to plan actions thatcan handle the dynamic nature of changing environments and are robust toperceptual errors. This thesis focuses on the design of algorithms to facilitatemotion planning in human environments and rigid object manipulation.Understanding human motion is a necessary first step to be able to performmotion planning in spaces that are inhabited by humans. Specifically throughlong-term prediction a robot should be able to plan collision-avoiding paths tocarry out whatever tasks are required of it. In this thesis we present a methodto classify motions by clustering paths, together with a method to translatethe resulting clusters into motion patterns that can be used to predict motion.Another challenge of robotics is the manipulation of everyday objects.Even in the realm of rigid objects, safe object-manipulation by either grippersor dexterous robotic hands requires complex physical parameter estimation.Such estimations are often error-prone and misestimations may cause completefailure to execute the desired task. Caging is presented as an alternativeapproach to classical manipulation by employing topological invariants todetermine whether an object is secured with only bounded mobility. Wepresent a method to decide whether a rigid object is in fact caged by a givengrasp or not, relying only on a rough approximation of the object and thegripper.

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