ﻻ يوجد ملخص باللغة العربية
This paper studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL). DRL exhibits huge potential in mapless navigation, but DRL agents performing well in training scenarios are found to perform poorly in unfamiliar real-world scenarios. In this work, we present the representation of LiDAR readings as a key factor behind agents performance degradation and propose a simple but powerful input pre-processing (IP) approach to improve the agents performance. As this approach uses adaptively parametric reciprocal functions to pre-process LiDAR readings, we refer to this approach as IPAPRec and its normalized version as IPAPRecN. IPAPRec/IPAPRecN can highlight important short-distance values and compress the range of less-important long-distance values in laser scans, which well addressed the issues induced by conventional representations of laser scans. Their high performance is validated by extensive simulation and real-world experiments. The results show that our methods can substantially improve agents success rates and greatly reduce the training time compared to conventional methods.
Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous meth
We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robots perspective while social-safety to measure the
We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent. We apply this approach to robotic manipulation tasks and train end-to-end visuomotor policies tha
This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation environment, a convolutional neural network (CNN) is trained to predict pr
Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation policies on legged robots can be challenging due to their complex dynami