Social-Navigation-PyEnvs

A framework used to train robots within a social navigation context with a wide range of human motion models to simulate crowds of pedestrians.

Description

This repository contains a framework developed starting from CrowdNav [1] and Python-RVO2 [2] used to train and test learning-based algorithms for Social Navigation.

In order to simulate crowds of pedestrians the following models are implemented:

• Social Force Model (SFM) [3] and its variations [4], [5]
• Headed Social Force Model (HSFM) [6]
• Optimal Reciprocal Collision Avoidance (ORCA) [7]

The CrowdNav module [1] includes the following reinforcement learning algorithms for social robot navigation:

• Collision Avoidance with Deep RL (CADRL) [8]
• Long-short term memory RL (LSTM-RL) [9]
• Social Attentive RL (SARL) [10]

The simulator is built upon Pygame in order to provide a functional visualization tool and OpenAI Gym, which defines the standard API for RL environments.

The simulator also implements a laser sensor and a differential drive robot, which allow users to develop sensor-based algorithms.

References

• [1] CrowdNav.
• [2] Python-RVO2.
• [3] Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(6803), 487-490.
• [4] Moussaïd, M., Helbing, D., Garnier, S., Johansson, A., Combe, M., & Theraulaz, G. (2009). Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proceedings of the Royal Society B: Biological Sciences, 276(1668), 2755-2762.
• [5] Guo, R. Y. (2014). Simulation of spatial and temporal separation of pedestrian counter flow through a bottleneck. Physica A: Statistical Mechanics and its Applications, 415, 428-439.
• [6] Farina, F., Fontanelli, D., Garulli, A., Giannitrapani, A., & Prattichizzo, D. (2017). Walking ahead: The headed social force model. PloS one, 12(1), e0169734.
• [7] Van Den Berg, J., Snape, J., Guy, S. J., & Manocha, D. (2011, May). Reciprocal collision avoidance with acceleration-velocity obstacles. In 2011 IEEE International Conference on Robotics and Automation (pp. 3475-3482). IEEE.
• [8] Chen, Y. F., Liu, M., Everett, M., & How, J. P. (2017, May). Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 285-292). IEEE.
• [9] Everett, M., Chen, Y. F., & How, J. P. (2018, October). Motion planning among dynamic, decision-making agents with deep reinforcement learning. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3052-3059). IEEE.
• [10] Chen, C., Liu, Y., Kreiss, S., & Alahi, A. (2019, May). Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning. In 2019 international conference on robotics and automation (ICRA) (pp. 6015-6022). IEEE.