# SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning

arXiv: https://arxiv.org/abs/2407.17460 · HTML: https://mlsanity.com/paper/2407.17460/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 30 of 30)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Adaptive Conformal Inference Under Distribution Shift](https://mlsanity.com/paper/2106.00170/) (2021)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Conformal Inference for Online Prediction with Arbitrary Distribution Shifts](https://mlsanity.com/paper/2208.08401/) (2022)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research](https://mlsanity.com/paper/2305.09304/) (2023)
- [Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation](https://mlsanity.com/paper/2202.06558/) (2022)
- [LOKI: Long Term and Key Intentions for Trajectory Prediction](https://mlsanity.com/paper/2108.08236/) (2021)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Adaptive Conformal Prediction for Motion Planning among Dynamic Agents](https://mlsanity.com/paper/2212.00278/) (2022)
- [A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges](https://mlsanity.com/paper/2311.06922/) (2023)
- [Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning](https://mlsanity.com/paper/2010.08600/) (2020)
- [Towards Safe Reinforcement Learning with a Safety Editor Policy](https://mlsanity.com/paper/2201.12427/) (2022)
- [Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians](https://mlsanity.com/paper/2102.13073/) (2021)
- [Safe Reinforcement Learning Using Black-Box Reachability Analysis](https://mlsanity.com/paper/2204.07417/) (2022)
- [Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation](https://mlsanity.com/paper/2401.12275/) (2024)
- [DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data](https://mlsanity.com/paper/2004.14079/) (2020)
- [Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting](https://mlsanity.com/paper/2109.14128/) (2021)
- [Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction](https://mlsanity.com/paper/2304.00194/) (2023)
- [Conformal Predictive Safety Filter for RL Controllers in Dynamic Environments](https://mlsanity.com/paper/2306.02551/) (2023)
- [Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments](https://mlsanity.com/paper/2309.13893/) (2023)
- [Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints](https://mlsanity.com/paper/2306.01075/) (2023)
- [Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction](https://mlsanity.com/paper/2107.07056/) (2021)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts](https://mlsanity.com/paper/2311.01457/) (2023)
- [Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space](https://mlsanity.com/paper/2405.13969/) (2024)

## Built on by (most cited first, 9 of 9)

- [Generative AI for Autonomous Driving: Frontiers and Opportunities](https://mlsanity.com/paper/2505.08854/) (2025)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution](https://mlsanity.com/paper/2508.05616/) (2025)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [LISN: Language-Instructed Social Navigation with VLM-based Controller Modulating](https://mlsanity.com/paper/2512.09920/) (2025)
- [When Environments Shift: Safe Planning with Generative Priors and Robust Conformal Prediction](https://mlsanity.com/paper/2602.12616/) (2026)
- [Bridging Deep Reinforcement Learning and Motion Planning for Model-Free Navigation in Cluttered Environments](https://mlsanity.com/paper/2504.07283/) (2025)
- [VividAnimator: An End-to-End Audio and Pose-driven Half-Body Human Animation Framework](https://mlsanity.com/paper/2510.10269/) (2025)
- [Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds](https://mlsanity.com/paper/2608.10056/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) enables social robots to generate trajectories without relying on human-designed rules or interventions, making it generally more effective than rule-based systems in adapting to complex, dynamic real-world scenarios. However, social navigation is a safety-critical task that requires robots to avoid collisions with pedestrians, whereas existing RL-based solutions often fall short of ensuring safety in complex environments. In this paper, we propose SoNIC, which to the best of our knowledge is the first algorithm that integrates adaptive conformal inference (ACI) with constrained reinforcement learning (CRL) to enable safe policy learning for social navigation. Specifically, our method not only augments RL observations with ACI-generated nonconformity scores, which inform the agent of the quantified uncertainty but also employs these uncertainty estimates to effectively guide the behaviors of RL agents by using constrained reinforcement learning. This integration regulates the behaviors of RL agents and enables them to handle safety-critical situations. On the standard CrowdNav benchmark, our method achieves a success rate of 96.93%, which is 11.67% higher than the previous state-of-the-art RL method and results in 4.5 times fewer collisions and 2.8 times fewer intrusions to ground-truth human future trajectories as well as enhanced robustness in out-of-distribution scenarios. To further validate our approach, we deploy our algorithm on a real robot by developing a ROS2-based navigation system. Our experiments demonstrate that the system can generate robust and socially polite decision-making when interacting with both sparse and dense crowds. The video demos can be found on our project website: https://sonic-social-nav.github.io/.