SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning (2407.17460v2)

Abstract: 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/.

• The paper introduces SoNIC, merging Adaptive Conformal Inference with Constrained Reinforcement Learning to overcome safety challenges in social robot navigation.
• The method quantifies uncertainties with spatial buffering to steer clear of risky zones, effectively reducing collision risks in dynamic environments.
• Experimental results show SoNIC outperforming standard RL approaches in success rates, collision avoidance, and adaptability under varied pedestrian scenarios.

An Expert Review of "SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning"

The paper "SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning" stands as a significant contribution to the field of social robot navigation by addressing the vital challenge of safety in complex environments. The authors introduce a novel algorithm named SoNIC, which innovatively combines Adaptive Conformal Inference (ACI) and Constrained Reinforcement Learning (CRL) to enhance the safety and effectiveness of robot navigation through pedestrian-dense areas.

The paper highlights the inadequacies of traditional Reinforcement Learning (RL) algorithms in handling safety-critical tasks such as social navigation. Despite RL's ability to learn optimal behavior in dynamic environments, it has yet to demonstrate satisfactory performance in terms of safety when applied to the real world. SoNIC aims to fill this gap by integrating ACI's uncertainty quantification capability with CRL's robust policy learning framework, thereby providing significant improvements over existing methods.

Technical Insights and Methodology

SoNIC showcases a robust framework where ACI is utilized to quantify prediction uncertainties for pedestrian trajectories. Traditional RL methods often neglect these uncertainties, leading to potential safety risks. By incorporating ACI, SoNIC introduces a spatial buffer around human agents, effectively guiding RL agents away from uncertain and risky areas. This integration allows for precise handling of prediction errors, thereby bolstering the reliability and safety of navigation decisions.

The use of Conformal Prediction within ACI offers a distribution-free method to maintain predefined coverage probabilities for predictions, positioning DtACI as a vital component in SoNIC's architecture. DtACI's ability to adapt online to distribution shifts provides a considerable advantage in dynamic environments where human behaviors can be unpredictable.

A particular strength of SoNIC lies in its approach to addressing the sparse feedback problem inherent in CRL applications. By replacing direct constraints on collision rates with spatial relaxation—constraining cumulative intrusions into buffered zones—SoNIC transforms an inherently difficult optimization problem into a more tractable one. This not only facilitates convergence but also maintains a high standard of safety without necessitating dense feedback.

Experimental Evaluation

The paper details extensive experiments, assessing SoNIC's performance in both in-distribution and out-of-distribution (OOD) scenarios. Results demonstrate that SoNIC substantially outperforms baseline methods, including state-of-the-art RL algorithms. Notable improvements are observed in success rates, collision avoidance, and adherence to social norms.

In tests simulating environments with rushing pedestrians or alternate pedestrian behavior models, SoNIC's robustness to domain shifts is evident. Such adaptability is critical in real-world deployments where environmental variables cannot be strictly controlled.

Implications and Future Directions

SoNIC's approach to integrating ACI and CRL presents promising implications for the deployment of autonomous robots in human-populated environments. By extending CRL's applicability through innovative spatial relaxation and leveraging precise uncertainty quantification, this work lays a foundation for enhanced safety protocols in autonomous navigation systems.

For future exploration, the authors suggest incorporating additional layers of uncertainty such as perception errors, potentially leading to an end-to-end system encapsulating perception and navigational planning. Such advancements could further solidify SoNIC's applicability in diverse and challenging real-world settings.

In summary, this paper makes a substantial contribution to the advancement of safe and adaptive autonomous navigation. While the implementation and results are promising within the controlled experimental setups described, ongoing research and real-world testing will be crucial in validating and refining the approach for broader applications.