# Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2203.15640 · HTML: https://mlsanity.com/paper/2203.15640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [Imitating by generating: deep generative models for imitation of interactive tasks](https://mlsanity.com/paper/1910.06031/) (2019)
- [Careful with That! Observation of Human Movements to Estimate Objects Properties](https://mlsanity.com/paper/2103.01555/) (2021)
- [Property-Aware Robot Object Manipulation: a Generative Approach](https://mlsanity.com/paper/2106.04385/) (2021)
- [From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness](https://mlsanity.com/paper/2109.00460/) (2021)

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

- [Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation](https://mlsanity.com/paper/2208.02058/) (2022)

## Abstract (arXiv)

Object manipulation is a natural activity we perform every day. How humans handle objects can communicate not only the willfulness of the acting, or key aspects of the context where we operate, but also the properties of the objects involved, without any need for explicit verbal description. Since human intelligence comprises the ability to read the context, allowing robots to perform actions that intuitively convey this kind of information would greatly facilitate collaboration. In this work, we focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects, aiming to endow robots with the capability to show carefulness in their movements. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles, either associated with careful or not careful attitudes. This approach would allow next generation robots to select the most appropriate style of movement, depending on the perceived context, and autonomously generate their motor action execution.