# SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning

arXiv: https://arxiv.org/abs/2209.11789 · HTML: https://mlsanity.com/paper/2209.11789/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 20 of 20)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [Driving Policy Transfer via Modularity and Abstraction](https://mlsanity.com/paper/1804.09364/) (2018)
- [Uncertainty-Aware Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/1702.01182/) (2017)
- [Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation](https://mlsanity.com/paper/1709.10489/) (2017)
- [Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees](https://mlsanity.com/paper/1909.05304/) (2019)
- [Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning](https://mlsanity.com/paper/1909.07299/) (2019)
- [Safe Multi-Agent Reinforcement Learning via Shielding](https://mlsanity.com/paper/2101.11196/) (2021)
- [Deep Visual MPC-Policy Learning for Navigation](https://mlsanity.com/paper/1903.02749/) (2019)
- [Learning to Fly by Crashing](https://mlsanity.com/paper/1704.05588/) (2017)
- [Optimization-Based Collision Avoidance](https://mlsanity.com/paper/1711.03449/) (2017)
- [LaND: Learning to Navigate from Disengagements](https://mlsanity.com/paper/2010.04689/) (2020)
- [Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments](https://mlsanity.com/paper/2102.05382/) (2021)
- [Dynamic Channel: A Planning Framework for Crowd Navigation](https://mlsanity.com/paper/1903.00143/) (2019)
- [Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04727/) (2021)

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

- [Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning](https://mlsanity.com/paper/2405.13863/) (2024)
- [ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning](https://mlsanity.com/paper/2412.00396/) (2024)

## Abstract (arXiv)

Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcting the control commands sent by an operator. It combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn an effective corrective control action that is used in a focused search for collision-free trajectories, and to reduce the frequency of triggering automatic emergency braking. This novel setup enables the RL policy to learn safely and directly on mobile robots in a real-world indoor environment, minimizing actual crashes even during training. Our real-world experiments show that, when compared with several baselines, our approach enjoys a higher average speed, lower crash rate, less emergency intervention, smaller computation overhead, and smoother overall control.