mlsanityData to 14 Oct 2026

Data-driven architecture to encode information in the kinematics of robots and artificial avatars

arXiv 2403.06557 ↗First posted 11 Mar 2024eess.SY cs.LG cs.ROcited by 1

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Abstract (arXiv)

We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator. We validate our approach on an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 26 references that are in the map. First posted on arXiv 11 Mar 2024.

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2 of 26 references.

References · 2

  1. 2023Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learningcited by 5
  2. 2022CT-DQN: Control-Tutored Deep Reinforcement Learningcited by 3

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Citing papers per year: 2025: 1.

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  1. 2025Reproducing Human Individual Motor Signatures: A Data-Driven Approach for Repetitive Motioncited by 0