Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models

By Mark Pfeiffer, Ulrich Schwesinger, Hannes Sommer, Enric Galceran, Roland Yves Siegwart. 2016

108 citations recorded by the source corpus.

Subfields: Automotive Engineering, Ocean Engineering, Social Psychology

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Citations in the corpus

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…”
A Survey of State-Action Representations for Autonomous Driving (2018, background): “Thus, Kretzschmar et al. (2014, 2016); Pfei(cid:27)er et al. (2016) use cubic splines to represent the future trajectory of each moving entity.”
Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning (2018, methodology): “Beyond behavioral cloning, inverse reinforcement learning has been used in [10], [11], [34] to learn the underlying cooperation features from human data using the maximum entropy method.”
Planning and Decision-Making for Autonomous Vehicles (2018, background): “(115) described learning socially compliant motion planning and human behavior.”
Deep Local Trajectory Replanning and Control for Robot Navigation (2019, methodology): “In initial experiments, we considered combining the above features through concatenation [36, 11] or outer product operations [53, 54].”
Human motion trajectory prediction: a survey (2019, methodology): “In the following we review several inverse planning approaches that predict multi-agent motions (Kuderer et al. 2012; Kretzschmar et al. 2014; Pfeiffer et al. 2016; Ma et al. 2017; Lee et al. 2017).”
Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning (2017, methodology): “However, the requirement of running reinforcement learning in an inner loop makes IRL extremely expensive to run [2], [9], [10].”
From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots (2016, methodology): “The approaches by Kuderer et al. [9], Pfeiffer et al. [10] and Kretzschmar et al. [11] use the maximum entropy inverse reinforcement learning techniques to interaction-aware motion models.”
Social Attention: Modeling Attention in Human Crowds (2017, methodology): “model with IRL-based approaches such as [17], [18], [24],”
Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations (2018, background): “Similarly, Pfeiffer et al. [10] and Kretzschmar et al. [11] present approaches for navigation in dynamic environments based on IRL.”