# Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation

arXiv: https://arxiv.org/abs/1609.06838 · HTML: https://mlsanity.com/paper/1609.06838/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2016.

## Built on by (most cited first, 15 of 15)

- [From Cognition to Precognition: A Future-Aware Framework for Social Navigation](https://mlsanity.com/paper/2409.13244/) (2024)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (2024)
- [Human-Inspired Multi-Agent Navigation using Knowledge Distillation](https://mlsanity.com/paper/2103.10000/) (2021)
- [Exploiting Proximity-Aware Tasks for Embodied Social Navigation](https://mlsanity.com/paper/2212.00767/) (2022)
- [Following the Human Thread in Social Navigation](https://mlsanity.com/paper/2404.11327/) (2024)
- [Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning](https://mlsanity.com/paper/2408.14183/) (2024)
- [Multi-subgoal Robot Navigation in Crowds with History Information and Interactions](https://mlsanity.com/paper/2205.02003/) (2022)
- [Towards Quantification of Assurance for Learning-enabled Components](https://mlsanity.com/paper/2301.08980/) (2023)
- [Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation](https://mlsanity.com/paper/2308.14947/) (2023)
- [An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction](https://mlsanity.com/paper/2211.00817/) (2022)
- [Robot Crowd Navigation in Dynamic Environment with Offline Reinforcement Learning](https://mlsanity.com/paper/2312.11032/) (2023)
- [Learning to Navigate Socially Through Proactive Risk Perception](https://mlsanity.com/paper/2510.07871/) (2025)
- [Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.05483/) (2026)
- [GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation](https://mlsanity.com/paper/2606.10832/) (2026)
- [Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms](https://mlsanity.com/paper/2608.00625/) (2026)

## Abstract (arXiv)

High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.