Socially Aware Motion Planning with Deep Reinforcement Learning

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.

8 pages

Cited by in corpus (18)

• Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization
• A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
• Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios
• Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
• Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
• CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios
• Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
• Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
• Multi-Agent Reinforcement Learning with Multi-Step Generative Models
• Predicting Responses to a Robot's Future Motion using Generative Recurrent Neural Networks
• Stochastic Primal-Dual Q-Learning
• The Eigenoption-Critic Framework
• Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds
• Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation
• RRT* Combined with GVO for Real-time Nonholonomic Robot Navigation in Dynamic Environment
• An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios
• Human-Aware Navigation Planner for Diverse Human-Robot Contexts
• Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles