# Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

## TL;DR

This paper introduces a Bayesian Neural Network approach to detect uncertainty in robotic controllers trained from demonstrations, enabling better failure detection and adaptive data collection in diverse environments.

## Contribution

It demonstrates that Bayesian Neural Networks can effectively identify uncertain situations, improving robustness and data efficiency in robotic learning from demonstrations.

## Key findings

- Uncertainty detection correlates with controller performance failures.
- Using uncertainty to trigger fallback strategies improves data efficiency.
- Bayesian Neural Networks outperform standard models in high-dimensional tasks.

## Abstract

Diversity of environments is a key challenge that causes learned robotic controllers to fail due to the discrepancies between the training and evaluation conditions. Training from demonstrations in various conditions can mitigate---but not completely prevent---such failures. Learned controllers such as neural networks typically do not have a notion of uncertainty that allows to diagnose an offset between training and testing conditions, and potentially intervene. In this work, we propose to use Bayesian Neural Networks, which have such a notion of uncertainty. We show that uncertainty can be leveraged to consistently detect situations in high-dimensional simulated and real robotic domains in which the performance of the learned controller would be sub-par. Also, we show that such an uncertainty based solution allows making an informed decision about when to invoke a fallback strategy. One fallback strategy is to request more data. We empirically show that providing data only when requested results in increased data-efficiency.