# Sensor Fusion for Robot Control through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1703.04550 · HTML: https://mlsanity.com/paper/1703.04550/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Efficient and Robust LiDAR-Based End-to-End Navigation](https://mlsanity.com/paper/2105.09932/) (2021)
- [Transfer RL across Observation Feature Spaces via Model-Based Regularization](https://mlsanity.com/paper/2201.00248/) (2022)
- [Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving](https://mlsanity.com/paper/2203.02401/) (2022)
- [Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping](https://mlsanity.com/paper/2209.14271/) (2022)
- [Learning Stability Attention in Vision-based End-to-end Driving Policies](https://mlsanity.com/paper/2304.02733/) (2023)
- [Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents](https://mlsanity.com/paper/2212.09033/) (2022)

## Abstract (arXiv)

Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information coming from multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.