Socially aware motion planning with deep reinforcement learning
Yu Fan Chen,Michael Everett,Miao Liu,Jonathan P. How +3 more
- pp 1343-1350
TLDR
Using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms and is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Abstract:
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.read more
Citations
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Journal ArticleDOI
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin,Stephan Wäldchen,Alexander Binder,Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +7 more
TL;DR: The authors investigate how these methods approach learning in order to assess the dependability of their decision making and propose a semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines.
Proceedings ArticleDOI
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
TL;DR: This work extends the previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules, and introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size.
Journal ArticleDOI
Unmasking Clever Hans predictors and assessing what machines really learn.
Sebastian Lapuschkin,Stephan Wäldchen,Alexander Binder,Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +7 more
TL;DR: In this article, the authors apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games, and propose a semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines.
Proceedings ArticleDOI
Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning
TL;DR: This work proposes to rethink pairwise interactions with a self-attention mechanism, and jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework, and captures the Human- human interactions occurring in dense crowds that indirectly affects the robot’s anticipation capability.
Proceedings ArticleDOI
Safe Reinforcement Learning With Model Uncertainty Estimates
TL;DR: MC-Dropout and Bootstrapping are used to give computationally tractable and parallelizable uncertainty estimates and are embedded in a Safe Reinforcement Learning framework to form uncertainty-aware navigation around pedestrians, resulting in a collision avoidance policy that knows what it does not know and cautiously avoids pedestrians that exhibit unseen behavior.
References
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