We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making.

We represent these edges as a collection of parametric curves(i.e., lines, circles, and B-splines).

Weintroduce verification tasksintothelocalization prediction ofRepPoints, producing RepPoints v2,whichprovidesconsistent improvements of about 2.0 mAP over the original RepPoints on the COCO object detection benchmark using different backbones and training methods.

--We present a tool A UTONA Vthat automates the mapping, localization, and path-planning tasks for autonomous navigation of robots. The modular architecture allows easy integration of various algorithms for these tasks for comparison. We present the generated maps and path-plans by A UTONA Vin indoor simulation scenarios. I NTRODUCTION Autonomous navigation in robotics is of central importance in search and rescue operations [1], warehouse automation [2], surveillance in hazardous environments [3] etc. Perception, mapping, localization, path-planning, and control are the key tasks necessary for autonomous navigation. We focus on designing a generic navigation framework for autonomous robots while the main concern remains in addressing the motion planning problem given the robot dynamics.

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments.

It will be better to add more descriptions about RepPoints. It is hard for readers to follow the implementation details described in Sec 3.4 if they are not familiar with RepPoints. What is the meaning of "..., such that the first two points explicitly represent the top-left and bottom-right corner points." In my understanding, the authors still predict n sample points following RepPoints, but they adopt the first two points to do point-bbox transformation. I suggest the authors replace the description of "the first two points" with "the first two points in point sets R and R ". Describe the definition of RepPoints loss in the paper instead of appendix will make it more easy to understand the implementations.

Navigating narrow roads with oncoming vehicles is a significant challenge that has garnered considerable public interest. These scenarios often involve sections that cannot accommodate two moving vehicles simultaneously due to the presence of stationary vehicles or limited road width. Autonomous vehicles must therefore profoundly comprehend their surroundings to identify passable areas and execute sophisticated maneuvers. To address this issue, this paper presents a comprehensive model for such an intricate scenario. The primary contribution is the principle of road width occupancy minimization, which models the narrow road problem and identifies candidate meeting gaps. Additionally, the concept of homology classes is introduced to help initialize and optimize candidate trajectories, while evaluation strategies are developed to select the optimal gap and most efficient trajectory. Qualitative and quantitative simulations demonstrate that the proposed approach, SM-NR, achieves high scene pass rates, efficient movement, and robust decisions. Experiments conducted in tiny gap scenarios and conflict scenarios reveal that the autonomous vehicle can robustly select meeting gaps and trajectories, compromising flexibly for safety while advancing bravely for efficiency.