Physically Plausible Dynamic Full-Body Grasp Synthesis for Human-Object Interactions
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2022-09
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Master Thesis
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Abstract
Human motion synthesis is challenging, especially the generation of full-body human-object interactions, as it involves a diverse set of motor skills and objects. Recent methods have shown success in modelling grasping motions, but they either neglect the full-body constraint or only synthesise motions until obtaining a grasp of the object. This thesis addresses these shortcomings by incorporating the dynamics (physical forces) with a physical simulation of a humanoid with articulated fingers and an object. We model the task of approaching, grasping and lifting the object in the reinforcement learning setting. To that end, we extend two existing methods based on manual reward engineering and adversarial motion imitation. While the manual reward engineering approach only requires a grasp and target reference, the motion prior method relies on entire reference motion sequences. We empirically validate that our extension of the manual reward engineering method successfully solves the grasping and lifting task on the training data but exhibits unrealistic motions. Moreover, we demonstrate that adapting the adversarial motion prior method effectively increases the naturalism of full-body human-object grasp synthesis but can exhibit visual artefacts and has a lower task success rate. Further development of the motion prior method to mitigate these deficiencies promises an exciting future research direction.
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Examiner : Christen, Sammy
Examiner: Aksan, Emre
Examiner : Kocabas, Muhammed
Examiner: Hilliges, Otmar
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ETH Zurich, Advanced Interactive Technologies
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03979 - Hilliges, Otmar (ehemalig) / Hilliges, Otmar (former)