# Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour

arXiv: https://arxiv.org/abs/2106.01434 · HTML: https://mlsanity.com/paper/2106.01434/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Agent57: Outperforming the Atari Human Benchmark](https://mlsanity.com/paper/2003.13350/) (2020)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Distral: Robust Multitask Reinforcement Learning](https://mlsanity.com/paper/1707.04175/) (2017)
- [A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning](https://mlsanity.com/paper/1907.04964/) (2019)
- [Attentive Multi-Task Deep Reinforcement Learning](https://mlsanity.com/paper/1907.02874/) (2019)

## Built on by (most cited first, 2 of 2)

- [SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning](https://mlsanity.com/paper/2205.03130/) (2022)
- [Generalizing to New Tasks via One-Shot Compositional Subgoals](https://mlsanity.com/paper/2205.07716/) (2022)

## Abstract (arXiv)

Robots need to be able to work in multiple different environments. Even when performing similar tasks, different behaviour should be deployed to best fit the current environment. In this paper, We propose a new approach to navigation, where it is treated as a multi-task learning problem. This enables the robot to learn to behave differently in visual navigation tasks for different environments while also learning shared expertise across environments. We evaluated our approach in both simulated environments as well as real-world data. Our method allows our system to converge with a 26% reduction in training time, while also increasing accuracy.