# Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion

arXiv: https://arxiv.org/abs/2507.08112 · HTML: https://mlsanity.com/paper/2507.08112/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Language-conditioned policies and VLAs, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Placed in

- Offline RL and imitation learning from data / [Language-conditioned policies and VLAs](https://mlsanity.com/offlinerl/language-conditioned-policies-and-vlas/)

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots](https://mlsanity.com/paper/1609.07910/) (2016)
- [A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation](https://mlsanity.com/paper/1803.04119/) (2018)

## Built on by (most cited first, 1 of 1)

- [Real-Time Obstacle Avoidance for a Mobile Robot Using CNN-Based Sensor Fusion](https://mlsanity.com/paper/2509.08095/) (2025)

## Abstract (arXiv)

Obstacle avoidance is crucial for mobile robots' navigation in both known and unknown environments. This research designs, trains, and tests two custom Convolutional Neural Networks (CNNs), using color and depth images from a depth camera as inputs. Both networks adopt sensor fusion to produce an output: the mobile robot's angular velocity, which serves as the robot's steering command. A newly obtained visual dataset for navigation was collected in diverse environments with varying lighting conditions and dynamic obstacles. During data collection, a communication link was established over Wi-Fi between a remote server and the robot, using Robot Operating System (ROS) topics. Velocity commands were transmitted from the server to the robot, enabling synchronized recording of visual data and the corresponding steering commands. Various evaluation metrics, such as Mean Squared Error, Variance Score, and Feed-Forward time, provided a clear comparison between the two networks and clarified which one to use for the application.