510 research outputs found
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
We present an approach for mobile robots to learn to navigate in dynamic
environments with pedestrians via raw depth inputs, in a socially compliant
manner. To achieve this, we adopt a generative adversarial imitation learning
(GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our
approach overcomes the disadvantages of previous methods, as they heavily
depend on the full knowledge of the location and velocity information of nearby
pedestrians, which not only requires specific sensors, but also the extraction
of such state information from raw sensory input could consume much computation
time. In this paper, our proposed GAIL-based model performs directly on raw
depth inputs and plans in real-time. Experiments show that our GAIL-based
approach greatly improves the safety and efficiency of the behavior of mobile
robots from pure behavior cloning. The real-world deployment also shows that
our method is capable of guiding autonomous vehicles to navigate in a socially
compliant manner directly through raw depth inputs. In addition, we release a
simulation plugin for modeling pedestrian behaviors based on the social force
model.Comment: ICRA 2018 camera-ready version. 7 pages, video link:
https://www.youtube.com/watch?v=0hw0GD3lkA
Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation
Socially aware robot navigation, where a robot is required to optimize its
trajectory to maintain comfortable and compliant spatial interactions with
humans in addition to reaching its goal without collisions, is a fundamental
yet challenging task in the context of human-robot interaction. While existing
learning-based methods have achieved better performance than the preceding
model-based ones, they still have drawbacks: reinforcement learning depends on
the handcrafted reward that is unlikely to effectively quantify broad social
compliance, and can lead to reward exploitation problems; meanwhile, inverse
reinforcement learning suffers from the need for expensive human
demonstrations. In this paper, we propose a feedback-efficient active
preference learning approach, FAPL, that distills human comfort and expectation
into a reward model to guide the robot agent to explore latent aspects of
social compliance. We further introduce hybrid experience learning to improve
the efficiency of human feedback and samples, and evaluate benefits of robot
behaviors learned from FAPL through extensive simulation experiments and a user
study (N=10) employing a physical robot to navigate with human subjects in
real-world scenarios. Source code and experiment videos for this work are
available at:https://sites.google.com/view/san-fapl.Comment: To appear in IROS 202
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