Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models.
We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous works.
Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geometric information loss during imagery projection.
We propose a novel joint-learning framework that can be implemented in a decentralized fashion, with generalization guarantees for certain function classes.
A crucial task in scene understanding is 3D object detection, which aims to detect and localize the 3D bounding boxes of objects belonging to specific classes.
In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information.
We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension.
Ranked #19 on Monocular 3D Object Detection on KITTI Cars Moderate