From Cognition to Precognition: A Future-Aware Framework for Social Navigation (2409.13244v2)

Abstract: To navigate safely and efficiently in crowded spaces, robots should not only perceive the current state of the environment but also anticipate future human movements. In this paper, we propose a reinforcement learning architecture, namely Falcon, to tackle socially-aware navigation by explicitly predicting human trajectories and penalizing actions that block future human paths. To facilitate realistic evaluation, we introduce a novel SocialNav benchmark containing two new datasets, Social-HM3D and Social-MP3D. This benchmark offers large-scale photo-realistic indoor scenes populated with a reasonable amount of human agents based on scene area size, incorporating natural human movements and trajectory patterns. We conduct a detailed experimental analysis with the state-of-the-art learning-based method and two classic rule-based path-planning algorithms on the new benchmark. The results demonstrate the importance of future prediction and our method achieves the best task success rate of 55% while maintaining about 90% personal space compliance. We will release our code and datasets. Videos of demonstrations can be viewed at https://zeying-gong.github.io/projects/falcon/ .

• The paper introduces the Falcon architecture, which integrates trajectory prediction and trajectory obstruction penalties to enhance socially-aware navigation.
• The research presents the SocialNav benchmark with realistic indoor datasets to rigorously evaluate navigation performance in human-populated environments.
• Empirical evaluations show a 55% task success rate and 90% compliance with personal space, highlighting Falcon’s effectiveness in complex social settings.

From Cognition to Precognition: A Future-Aware Framework for Social Navigation

The paper introduces a novel reinforcement learning architecture for socially-aware navigation named Falcon. This architecture aims to address the challenges autonomous robots face when navigating crowded environments by not only perceiving the current state of the surroundings but also anticipating future human movements. The methodology is grounded in the integration of trajectory prediction and the introduction of penalties for actions that might block future human paths.

Core Contributions

The core contributions of this research are threefold:

1. Falcon Architecture: Falcon is designed to explicitly predict human trajectories, ensuring that the robot's navigation strategy remains socially compliant and minimizes disturbances to human movement. The system's novelty lies in its two-pronged strategy: introducing a trajectory obstruction penalty, and employing a Spatial-Temporal Precognition Module during training that incorporates trajectory prediction of humans as an auxiliary task. This approach ensures that the robot is continuously aware of future dynamics in real-time, facilitating more informed and socially considerate navigation choices.
2. SocialNav Benchmark: The research highlights the introduction of a new benchmark, SocialNav, to evaluate robotic navigation in socially complex environments. This benchmark comprises two datasets, Social-HM3D and Social-MP3D, which offer large-scale, photo-realistic indoor scenes populated with human agents demonstrating natural movement patterns appropriate to the space's size. By providing diverse and realistic training environments, the benchmark enables a rigorous evaluation of robotic navigation performance under social constraints.
3. Empirical Evaluation and Results: The paper presents an extensive empirical paper comparing Falcon against state-of-the-art learning-based methods as well as traditional rule-based algorithms. Through this evaluation on the SocialNav benchmark, Falcon is shown to outperform its counterparts with a task success rate of 55%, while maintaining compliance with personal space in about 90% of instances. These results emphasize the efficacy of future trajectory prediction in enhancing the robot's ability to navigate socially complex environments efficiently.

Implications and Future Directions

The implications of this research are significant in both practical applications and theoretical advancements in the field of socially-aware navigation. Practically, the Falcon architecture enhances the ability of robots to operate in environments where human-robot interaction is unavoidable, such as hospitals, shopping malls, or airports. By directly addressing the need for trajectory prediction, the paper advances the autonomy capabilities of robotic systems, making them safer and more effective in shared human spaces.

Theoretically, this work encourages further exploration into the integration of predictive mechanisms in reinforcement learning, particularly in scenarios demanding social compliance. Future developments can build upon these insights to explore more complex behaviors involving human interaction, such as cooperative tasks or even anticipating unplanned human movements.

In conclusion, this paper contributes a significant advancement to the domain of social navigation by introducing an architecture that equips autonomous systems with the capability to foresee human movements effectively. Through realistic benchmarking, Falcon demonstrates the practicality and potential of predictive reinforcement learning methods in enhancing robotic social navigation. This research constitutes a step forward in making robots harmoniously co-inhabit human-centered environments.