We propose CLEVER, an active learning system for robust semantic perception with Deep Neural Networks (DNNs). For data arriving in streams, our system seeks human support when encountering failures and adapts DNNs online based on human instructions. In this way, CLEVER can eventually accomplish the given semantic perception tasks. Our main contribution is the design of a system that meets several desiderata of realizing the aforementioned capabilities. The key enabler herein is our Bayesian formulation that encodes domain knowledge through priors. Empirically, we not only motivate CLEVER's design but further demonstrate its capabilities with a user validation study as well as experiments on humanoid and deformable objects. To our knowledge, we are the first to realize stream-based active learning on a real robot, providing evidence that the robustness of the DNN-based semantic perception can be improved in practice. The project website can be accessed at https://sites.google.com/view/thecleversystem.

--In this study, we present a novel LiDAR-based semantic segmentation framework tailored for autonomous forklifts operating in complex outdoor environments. Central to our approach is the integration of a dual LiDAR system, which combines forward-facing and downward-angled LiDAR sensors to enable comprehensive scene understanding, specifically tailored for industrial material handling tasks. The dual configuration improves the detection and segmentation of dynamic and static obstacles with high spatial precision. Using high-resolution 3D point clouds captured from two sensors, our method employs a lightweight yet robust approach that segments the point clouds into safety-critical instance classes such as pedestrians, vehicles, and forklifts, as well as environmental classes such as driveable ground, lanes, and buildings. Experimental validation demonstrates that our approach achieves high segmentation accuracy while satisfying strict runtime requirements, establishing its viability for safety-aware, fully autonomous forklift navigation in dynamic warehouse and yard environments.

Mobile intelligent robots are being deployed in increasingly unstructured environments, where they are expected to work out complex and dynamic tasks such as autonomous movement and mobile manipulation. Such learning-based robots not only need to acquire basic information about their environments, but must also build this understanding with respect to factors such as object detection and semantic classification. Typically, a static model pretrained on a variety of data is deployed in a particular learning-based robot system. A robot expected to understand semantics, i.e. what is happening in a scene, would therefore learn how to do so during its pretraining phase. This approach poses three main challenges: the model may need to be retrained to incorporate new data; acquired knowledge should be preserved while adapting to new tasks and environments; and training signals of the environment are required during deployment.