# L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation

arXiv: https://arxiv.org/abs/2003.09207 · HTML: https://mlsanity.com/paper/2003.09207/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm](https://mlsanity.com/paper/2105.14125/) (2021)
- [View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements](https://mlsanity.com/paper/2111.05060/) (2021)
- [ViewBirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric view](https://mlsanity.com/paper/2210.06332/) (2022)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Breaking the Bias Barrier in Concave Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2603.08518/) (2026)
- [Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2211.15901/) (2022)
- [Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling](https://mlsanity.com/paper/2608.12917/) (2026)

## Abstract (arXiv)

This work presents a deep reinforcement learning framework for interactive navigation in a crowded place. Our proposed approach, Learning to Balance (L2B) framework enables mobile robot agents to steer safely towards their destinations by avoiding collisions with a crowd, while actively clearing a path by asking nearby pedestrians to make room, if necessary, to keep their travel efficient. We observe that the safety and efficiency requirements in crowd-aware navigation have a trade-off in the presence of social dilemmas between the agent and the crowd. On the one hand, intervening in pedestrian paths too much to achieve instant efficiency will result in collapsing a natural crowd flow and may eventually put everyone, including the self, at risk of collisions. On the other hand, keeping in silence to avoid every single collision will lead to the agent's inefficient travel. With this observation, our L2B framework augments the reward function used in learning an interactive navigation policy to penalize frequent active path clearing and passive collision avoidance, which substantially improves the balance of the safety-efficiency trade-off. We evaluate our L2B framework in a challenging crowd simulation and demonstrate its superiority, in terms of both navigation success and collision rate, over a state-of-the-art navigation approach.