# Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories

arXiv: https://arxiv.org/abs/2204.03051 · HTML: https://mlsanity.com/paper/2204.03051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 6 Apr 2022.

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

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [Learning from Demonstration with Weakly Supervised Disentanglement](https://mlsanity.com/paper/2006.09107/) (2020)
- [A Survey on Deep Learning Methods for Robot Vision](https://mlsanity.com/paper/1803.10862/) (2018)
- [How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents](https://mlsanity.com/paper/2110.03608/) (2021)

## Abstract (arXiv)

We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e.g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot. In the first stage, we perceive and pre-process the given inputs, isolating individual commands from the complete instruction provided by a human user. In the second stage we encode the individual commands into a multimodal latent space, employing a deep generative model. Finally, in the third stage we convert the multimodal latent values into individual trajectories and combine them into a single dynamic movement primitive, allowing its execution in a robotic platform. We evaluate our pipeline in the context of a novel robotic handwriting task, where the robot receives as input a word through different perceptual modalities (e.g., image, sound), and generates the corresponding motion trajectory to write it, creating coherent and readable handwritten words.