# Curriculum Learning for Safe Mapless Navigation

arXiv: https://arxiv.org/abs/2112.12490 · HTML: https://mlsanity.com/paper/2112.12490/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 23 Dec 2021.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Algorithms for Verifying Deep Neural Networks](https://mlsanity.com/paper/1903.06758/) (2019)
- [Formal Security Analysis of Neural Networks using Symbolic Intervals](https://mlsanity.com/paper/1804.10829/) (2018)
- [Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning](https://mlsanity.com/paper/1511.06342/) (2015)
- [Efficient Formal Safety Analysis of Neural Networks](https://mlsanity.com/paper/1809.08098/) (2018)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Reachability Analysis of Deep Neural Networks with Provable Guarantees](https://mlsanity.com/paper/1805.02242/) (2018)
- [Measuring Neural Net Robustness with Constraints](https://mlsanity.com/paper/1605.07262/) (2016)
- [Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments](https://mlsanity.com/paper/1612.05533/) (2016)
- [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 Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms](https://mlsanity.com/paper/1904.06979/) (2019)
- [Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning](https://mlsanity.com/paper/1812.05027/) (2018)
- [Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/1804.00456/) (2018)
- [Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks](https://mlsanity.com/paper/2102.04022/) (2021)
- [Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-Based Robot Navigation](https://mlsanity.com/paper/2004.14684/) (2020)
- [Sim-to-Real Transfer for Optical Tactile Sensing](https://mlsanity.com/paper/2004.00136/) (2020)

## Built on by (most cited first, 11 of 11)

- [Verifying Learning-Based Robotic Navigation Systems](https://mlsanity.com/paper/2205.13536/) (2022)
- [The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks](https://mlsanity.com/paper/2301.07068/) (2023)
- [Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees](https://mlsanity.com/paper/2308.09842/) (2023)
- [Eurekaverse: Environment Curriculum Generation via Large Language Models](https://mlsanity.com/paper/2411.01775/) (2024)
- [Safe Deep Reinforcement Learning by Verifying Task-Level Properties](https://mlsanity.com/paper/2302.10030/) (2023)
- [Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation](https://mlsanity.com/paper/2302.06695/) (2023)
- [Reinforcement Learning by Guided Safe Exploration](https://mlsanity.com/paper/2307.14316/) (2023)
- [ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks](https://mlsanity.com/paper/2407.01639/) (2024)
- [Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation](https://mlsanity.com/paper/2505.05235/) (2025)
- [Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification](https://mlsanity.com/paper/2507.05405/) (2025)
- [Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing](https://mlsanity.com/paper/2312.05890/) (2023)

## Abstract (arXiv)

This work investigates the effects of Curriculum Learning (CL)-based approaches on the agent's performance. In particular, we focus on the safety aspect of robotic mapless navigation, comparing over a standard end-to-end (E2E) training strategy. To this end, we present a CL approach that leverages Transfer of Learning (ToL) and fine-tuning in a Unity-based simulation with the Robotnik Kairos as a robotic agent. For a fair comparison, our evaluation considers an equal computational demand for every learning approach (i.e., the same number of interactions and difficulty of the environments) and confirms that our CL-based method that uses ToL outperforms the E2E methodology. In particular, we improve the average success rate and the safety of the trained policy, resulting in 10% fewer collisions in unseen testing scenarios. To further confirm these results, we employ a formal verification tool to quantify the number of correct behaviors of Reinforcement Learning policies over desired specifications.