Online Context Learning for Socially-compliant Navigation

Robot social navigation needs to adapt to different human factors and environmental contexts. However, since these factors and contexts are difficult to predict and cannot be exhaustively enumerated, traditional learning-based methods have difficulty in ensuring the social attributes of robots in long-term and cross-environment deployments. This letter introduces an online context learning method that aims to empower robots to adapt to new social environments online. The proposed method adopts a two-layer structure. The bottom layer is built using a deep reinforcement learning-based method to ensure the output of basic robot navigation commands. The upper layer is implemented using an online robot learning-based method to socialize the control commands suggested by the bottom layer. Experiments using a community-wide simulator show that our method outperforms the state-of-the-art ones. Experimental results in the most challenging scenarios show that our method improves the performance of the state-of-the-art by 8%. The source code of the proposed method, the data used, and the tools for the per-training step will be publicly available at https://github.com/Nedzhaken/SOCSARL-OL.

Software-hardware Integration and Human-centered Benchmarking for Socially-compliant Robot Navigation

The social compatibility (SC) is one of the most important parameters for service robots. It characterises the interaction quality between a robot and a human. In this paper, we first introduce an open-source software-hardware integration scheme for socially-compliant robot navigation and then propose a human-centered benchmarking framework. For the former, we integrate one 3D lidar, one 2D lidar, and four RGB-D cameras for robot exterior perception. The software system is entirely based on the Robot Operating System (ROS) with high modularity and fully deployed to the embedded hardware-based edge while running at a rate that exceeds the release frequency of sensor data. For the latter, we propose a new human-centered performance evaluation metric that can be used to measure SC quickly and efficiently. The values of this metric correlate with the results of the Godspeed questionnaire, which is believed to be a golden standard approach for SC measurements. Together with other commonly used metrics, we benchmark two open-source socially-compliant robot navigation methods, in an end-to-end manner. We clarify all aspects of the benchmarking to ensure the reproducibility of the experiments. We also show that the proposed new metric can provide further justification for the selection of numerical metrics (objective) from a human perspective (subjective).

Efficient Online Transfer Learning for 3D Object Classification in Autonomous Driving

Autonomous driving has achieved rapid development over the last few decades, including the machine perception as an important issue of it. Although object detection based on conventional cameras has achieved remarkable results in 2D/3D, non-visual sensors such as 3D LiDAR still have incomparable advantages in the accuracy of object position detection. However, the challenge also exists with the difficulty in properly interpreting point cloud generated by LiDAR. This paper presents a multi-modal-based online learning system for 3D LiDAR-based object classification in urban environments, including cars, cyclists and pedestrians. The proposed system aims to effectively transfer the mature detection capabilities based on visual sensors to the new model learning based on non-visual sensors through a multi-target tracker (i.e. using one sensor to train another). In particular, it integrates the Online Random Forests (ORF) \cite{saffari2009line} method, which inherently has the abilities of fast and multi-class learning. Through experiments, we show that our system is capable of learning a high-performance model for LiDAR-based 3D object classification on-the-fly, which is especially suitable for robotics in-situ deployment while responding to the widespread challenge of insufficient detector generalization capabilities.

EU Long-term Dataset with Multiple Sensors for Autonomous Driving

The field of autonomous driving has grown tremendously over the past few years, along with the rapid progress in sensor technology. One of the major purposes of using sensors is to provide environment perception for vehicle understanding, learning and reasoning, and ultimately interacting with the environment. In this article, we introduce a multisensor framework allowing vehicle to perceive its surroundings and locate itself in a more efficient and accurate way. Our framework integrates up to eleven heterogeneous sensors including various cameras and lidars, a radar, an IMU (Inertial Measurement Unit), and a GPS/RTK (Global Positioning System / Real-Time Kinematic), while exploits a ROS (Robot Operating System) based software to process the sensory data. In addition, we present a new dataset (https://epan-utbm.github.io/utbm_robocar_dataset/) for autonomous driving captured many new research challenges (e.g. highly dynamic environment), and especially for long-term autonomy (e.g. creating and maintaining maps), collected with our instrumented vehicle, publicly available to the community.