Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning

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A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “In robotics, the formulation of IRL offers an efﬁcient solution for learning policies for socially compliant navigation (Okal and Arras, 2016; Pfeiffer et al., 2016; Kretzschmar et al., 2016), where the agent needs to not only avoid collisions with static obstacles but also to behave in a socially…”
Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning (2017, background): “Agents are also able to learn from expert demonstrations via an intermediate step of learning the latent cost or the reward function [2], [9], [10] through inverse reinforcement learning [11], [12].”
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks (2016, background): “This also means that humans should not just be regarded as obstacles and that an optimal robot motion also must consider human comfort metrics [3].”
Deep Local Trajectory Replanning and Control for Robot Navigation (2019, unknown): “Generally, most methods for social navigation explicitly model interactions among agents or social conventions [5, 31, 7, 32, 33, 34, 35, 36, 37, 38, 39].”