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

arXiv: https://arxiv.org/abs/2403.06557 · HTML: https://mlsanity.com/paper/2403.06557/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 2 of 2)

- [Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/2311.10026/) (2023)
- [CT-DQN: Control-Tutored Deep Reinforcement Learning](https://mlsanity.com/paper/2212.01343/) (2022)

## Built on by (most cited first, 1 of 1)

- [Reproducing Human Individual Motor Signatures: A Data-Driven Approach for Repetitive Motion](https://mlsanity.com/paper/2503.15225/) (2025)

## 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.