Abstract
With mobile robots becoming more prevalent in our daily lives, it is crucial that these robots navigate in a safe and socially-aware manner. While recent works have shown promising results by using Deep Reinforcement Learning (DRL) techniques to learn socially-aware navigation policies, most approaches are limited to local, short-term navigation. For more complex settings, DRL approaches rely on subgoals often computed using traditional path planners. However, these planners are not necessarily suitable for social navigation since they rarely consider the pedestrians in the scene and often disregard the long term cost of a path or the pedestrian dynamics. In this paper, we present an alternative global planner that uses a learnt local cost predictor to generate subgoal guidance to help a DRL robot to make progress towards its goal while also taking into account the pedestrians in the environment. We evaluate the proposed approach in simulation. We consider several environments of varying complexity as well as different pedestrian behaviours. Our results show that a DRL robot using the proposed planner is less likely to collide with pedestrians and exhibits improved social awareness when compared to a baseline approach using traditional path planner methods.
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Notes
- 1.
The robot is controlled by the DRL short-range navigation policy.
- 2.
In this work, each robot state \(q_i\) contains the robot’s location (\(x_i, y_i\)) in the world frame.
References
Bansal, S., et al.: Combining optimal control and learning for visual navigation in novel environments. In: CoRL (2020)
van den Berg, J., et al.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE ICRA, pp. 1928–1935 (2008)
Boor, V., et al.: The Gaussian sampling strategy for probabilistic roadmap planners. In: Proceedings 1999 IEEE ICRA, vol. 2, pp. 1018–1023 (1999)
Brito, B., et al.: Where to go next: Learning a subgoal recommendation policy for navigation among pedestrians. arXiv:abs/2102.13073 (2021)
Brockman, G., et al.: Openai gym (2016)
Chen, Y.F., et al.: Socially aware motion planning with deep reinforcement learning. IEEE/RSJ IROS, pp. 1343–1350 (2017)
Chiang, H.T.L., et al.: Learning navigation behaviors end-to-end with autorl. IEEE Robot. Autom. Lett. 4, 2007–2014 (2019)
Ciou, P.H., et al.: Composite reinforcement learning for social robot navigation. In: 2018 IEEE/RSJ IROS, pp. 2553–2558 (2018)
Everett, M., et al.: Motion planning among dynamic, decision-making agents with deep reinforcement learning. In: 2018 IEEE/RSJ IROS, pp. 3052–3059 (2018)
Faust, A., et al.: Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning. In: IEEE ICRA, pp. 5113–5120 (2018)
Guldenring, R., et al.: Learning local planners for human-aware navigation in indoor environments. In: 2020 IEEE/RSJ IROS), pp. 6053–6060. IEEE (2020)
Guo, X., et al.: On the class imbalance problem. In: 2008 Fourth International Conference on Natural Computation, vol. 4, pp. 192–201 (2008)
Hart, P.E., et al.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybernetics 4(2), 100–107 (1968)
Kavraki, L., Latombe, J.C.: Randomized preprocessing of configuration for fast path planning. In: IEEE ICRA, vol. 3, pp. 2138–2145 (1994)
Kirby, R., et al.: Affective social robots. Robot. Auton. Syst. 58(3), 322–332 (2010)
Kruse, T., et al.: Human-aware robot navigation: A survey. Robot. Auton. Syst. 61(12), 1726–1743 (2013)
Kästner, L., et al.: Connecting deep-reinforcement-learning-based obstacle avoidance with conventional global planners using waypoint generators. In: 2021 IEEE/RSJ IROS, pp. 1213–1220 (2021)
LaValle, S.M.: Rapidly-exploring random trees: A new tool for path planning. The Annual Research Report (1998)
Li, C., et al.: igibson 2.0: Object-centric simulation for robot learning of everyday household tasks. arXiv:abs/2108.03272 (2021)
Mavrogiannis, C., et al.: Core challenges of social robot navigation: A survey. arXiv:abs/2103.05668 (2021)
Pérez-D’Arpino, C., et al.: Robot navigation in constrained pedestrian environments using reinforcement learning. In: IEEE ICRA, pp. 1140–1146 (2021)
Raffin, A., et al.: Stable-baselines3: Reliable reinforcement learning implementations. J. Mach. Learn. Res. 22(268), 1–8 (2021)
Randhavane, T., et al.: Pedestrian dominance modeling for socially-aware robot navigation. In: IEEE ICRA, pp. 5621–5628 (2019)
Regier, P., et al.: Deep reinforcement learning for navigation in cluttered environments. In: CS & IT, pp. 193–204 (2020)
Sisbot, E.A., et al.: A human aware mobile robot motion planner. IEEE TRO 23(5), 874–883 (2007)
Wang, J., et al.: Metrics for evaluating social conformity of crowd navigation algorithms. In: IEEE ARSO, p. 1–6. IEEE Press (2022)
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Ah Sen, N., Carreno-Medrano, P., Kulić, D. (2022). Human-Aware Subgoal Generation in Crowded Indoor Environments. In: Cavallo, F., et al. Social Robotics. ICSR 2022. Lecture Notes in Computer Science(), vol 13817. Springer, Cham. https://doi.org/10.1007/978-3-031-24667-8_5
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