# Interpretable Latent Spaces for Learning from Demonstration

arXiv: https://arxiv.org/abs/1807.06583 · HTML: https://mlsanity.com/paper/1807.06583/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning](https://mlsanity.com/paper/2010.14500/) (2020)
- [A Workflow for Offline Model-Free Robotic Reinforcement Learning](https://mlsanity.com/paper/2109.10813/) (2021)
- [Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations](https://mlsanity.com/paper/2112.02290/) (2021)
- [Disentangled Relational Representations for Explaining and Learning from Demonstration](https://mlsanity.com/paper/1907.13627/) (2019)
- [Learning from Demonstration with Weakly Supervised Disentanglement](https://mlsanity.com/paper/2006.09107/) (2020)
- [Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder](https://mlsanity.com/paper/2210.09959/) (2022)
- [Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE](https://mlsanity.com/paper/2111.10954/) (2021)
- [Preventing Imitation Learning with Adversarial Policy Ensembles](https://mlsanity.com/paper/2002.01059/) (2020)

## Abstract (arXiv)

Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained within instructions) in a corresponding high-dimensional sensory input stream from the world. Models such as deep neural networks, with high capacity through their large parameter spaces, can be used to compress the high-dimensional sensory data to lower dimensional representations. These low-dimensional representations facilitate symbol grounding, but may not guarantee that the representation would be human-interpretable. We propose a method which utilises the grouping of user-defined symbols and their corresponding sensory observations in order to align the learnt compressed latent representation with the semantic notions contained in the abstract labels. We demonstrate this through experiments with both simulated and real-world object data, showing that such alignment can be achieved in a process of physical symbol grounding.