# On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach

arXiv: https://arxiv.org/abs/2002.04109 · HTML: https://mlsanity.com/paper/2002.04109/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies](https://mlsanity.com/paper/2403.15267/) (2024)
- [Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies](https://mlsanity.com/paper/2206.05652/) (2022)
- [Denoising Diffusion Planner: Learning Complex Paths from Low-Quality Demonstrations](https://mlsanity.com/paper/2410.21497/) (2024)
- [Enhancing team performance with transfer-learning during real-world human-robot collaboration](https://mlsanity.com/paper/2211.13070/) (2022)
- [Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation](https://mlsanity.com/paper/2306.06192/) (2023)

## Abstract (arXiv)

We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information. The planner is trained using a reward function shaped based on the online knowledge of the map of the training environment, obtained using grid-based Rao-Blackwellized particle filter, in an attempt to enhance the obstacle awareness of the agent. The agent is trained in a complex simulated environment and evaluated in two unseen ones. We show that the policy trained using the introduced reward function not only outperforms standard reward functions in terms of convergence speed, by a reduction of 36.9% of the iteration steps, and reduction of the collision samples, but it also drastically improves the behaviour of the agent in unseen environments, respectively by 23% in a simpler workspace and by 45% in a more clustered one. Furthermore, the policy trained in the simulation environment can be directly and successfully transferred to the real robot. A video of our experiments can be found at: https://youtu.be/UEV7W6e6ZqI