Abstract-- This paper proposes an image-based visual ser-voing (IBVS) framework for UA V navigation and collision avoidance using only an RGB camera. While UA V navigation has been extensively studied, it remains challenging to apply IBVS in missions involving multiple visual targets and collision avoidance. The proposed method achieves navigation without explicit path planning, and collision avoidance is realized through AI-based monocular depth estimation from RGB images. Unlike approaches that rely on stereo cameras or external workstations, our framework runs fully onboard a Jetson platform, ensuring a self-contained and deployable system. Experimental results validate that the UA V can navigate across multiple AprilT ags and avoid obstacles effectively in GPS-denied environments. I. INTRODUCTION Most UA V applications depend on position estimation provided by global positioning systems (GPS). However, GPS is often unavailable in indoor, mountainous, or forest environments, motivating the use of computer vision for UA V navigation. This paper focuses on image-based visual servoing (IBVS) with an onboard RGB camera.

Abstract-- This letter introduces DiffAero, a lightweight, GPU-accelerated, and fully differentiable simulation framework designed for efficient quadrotor control policy learning. Dif-fAero supports both environment-level and agent-level parallelism and integrates multiple dynamics models, customizable sensor stacks (IMU, depth camera, and LiDAR), and diverse flight tasks within a unified, GPU-native training interface. By fully parallelizing both physics and rendering on the GPU, DiffAero eliminates CPU-GPU data transfer bottlenecks and delivers orders-of-magnitude improvements in simulation throughput. In contrast to existing simulators, DiffAero not only provides high-performance simulation but also serves as a research platform for exploring differentiable and hybrid learning algorithms. Extensive benchmarks and real-world flight experiments demonstrate that DiffAero and hybrid learning algorithms combined can learn robust flight policies in hours on consumer-grade hardware. Quadrotors--and swarms of quadrotors thereof--are increasingly deployed in complex environments for aerial inspection, environmental monitoring, and high-speed racing, owing to their agile maneuverability and onboard sensing capabilities. End-to-end learning addresses these limitations by training neural flight policies that map raw sensor observations directly to control commands, thereby streamlining the autonomy stack and enabling tighter feedback loops [4].

Modern robotic manipulation primarily relies on visual observations in a 2D color space for skill learning but suffers from poor generalization. In contrast, humans, living in a 3D world, depend more on physical properties-such as distance, size, and shape-than on texture when interacting with objects. Since such 3D geometric information can be acquired from widely available depth cameras, it appears feasible to endow robots with similar perceptual capabilities. Our pilot study found that using depth cameras for manipulation is challenging, primarily due to their limited accuracy and susceptibility to various types of noise. In this work, we propose Camera Depth Models (CDMs) as a simple plugin on daily-use depth cameras, which take RGB images and raw depth signals as input and output denoised, accurate metric depth. To achieve this, we develop a neural data engine that generates high-quality paired data from simulation by modeling a depth camera's noise pattern. Our results show that CDMs achieve nearly simulation-level accuracy in depth prediction, effectively bridging the sim-to-real gap for manipulation tasks. Notably, our experiments demonstrate, for the first time, that a policy trained on raw simulated depth, without the need for adding noise or real-world fine-tuning, generalizes seamlessly to real-world robots on two challenging long-horizon tasks involving articulated, reflective, and slender objects, with little to no performance degradation. We hope our findings will inspire future research in utilizing simulation data and 3D information in general robot policies.

Due to high-mix-low-volume production, sheet-metal workshops today are challenged by small series and varying orders. As standard automation solutions tend to fall short, SMEs resort to repetitive manual labour impacting production costs and leading to tech-skilled workforces not being used to their full potential. The COOCK+ ROBUST project aims to transform cobots into mobile and reconfigurable production assistants by integrating existing technologies, including 3D object recognition and localisation. This article explores both the opportunities and challenges of enhancing cobotic systems with these technologies in an industrial setting, outlining the key steps involved in the process. Additionally, insights from a past project, carried out by the ACRO research unit in collaboration with an industrial partner, serves as a concrete implementation example throughout.

Hand-eye calibration is an important task in vision-guided robotic systems and is crucial for determining the transformation matrix between the camera coordinate system and the robot end-effector. Existing methods, for multi-view robotic systems, usually rely on accurate geometric models or manual assistance, generalize poorly, and can be very complicated and inefficient. Therefore, in this study, we propose PlaneHEC, a generalized hand-eye calibration method that does not require complex models and can be accomplished using only depth cameras, which achieves the optimal and fastest calibration results using arbitrary planar surfaces like walls and tables. PlaneHEC introduces hand-eye calibration equations based on planar constraints, which makes it strongly interpretable and generalizable. PlaneHEC also uses a comprehensive solution that starts with a closed-form solution and improves it withiterative optimization, which greatly improves accuracy. We comprehensively evaluated the performance of PlaneHEC in both simulated and real-world environments and compared the results with other point-cloud-based calibration methods, proving its superiority. Our approach achieves universal and fast calibration with an innovative design of computational models, providing a strong contribution to the development of multi-agent systems and embodied intelligence.

RealSense, a depth-camera technology that basically disappeared within Intel, has returned as a separate company. The company has spun out from Intel and raised 50 million in funding. The company will be led by Nadav Orbach, Intel's former vice president and general Manager for the Incubation and Disruptive Innovation group. RealSense plans to address "increased demand for humanoid and autonomous mobile robotics (AMRs), as well as AI-powered access control and security solutions," the company said. RealSense flourished, so to speak, about a decade ago, when its depth-camera technology was competing with the Microsoft Kinect system.

Strawberry farming demands intensive labor for monitoring and maintaining plant health. To address this, Team SARAL develops an autonomous robot for the 2024 ASABE Student Robotics Challenge, capable of navigation, unhealthy leaf detection, and removal. The system addresses labor shortages, reduces costs, and supports sustainable farming through vision-based plant assessment. This work demonstrates the potential of robotics to modernize strawberry cultivation and enable scalable, intelligent agricultural solutions.

Ballbot (i.e. Ball balancing robot) navigation usually relies on methods rooted in control theory (CT), and works that apply Reinforcement learning (RL) to the problem remain rare while generally being limited to specific subtasks (e.g. balance recovery). Unlike CT based methods, RL does not require (simplifying) assumptions about environment dynamics (e.g. the absence of slippage between the ball and the floor). In addition to this increased accuracy in modeling, RL agents can easily be conditioned on additional observations such as depth-maps without the need for explicit formulations from first principles, leading to increased adaptivity. Despite those advantages, there has been little to no investigation into the capabilities, data-efficiency and limitations of RL based methods for ballbot control and navigation. Furthermore, there is a notable absence of an open-source, RL-friendly simulator for this task. In this paper, we present an open-source ballbot simulation based on MuJoCo, and show that with appropriate conditioning on exteroceptive observations as well as reward shaping, policies learned by classical model-free RL methods are capable of effectively navigating through randomly generated uneven terrain, using a reasonable amount of data (four to five hours on a system operating at 500hz).

This work presents a team description paper for the RoboCup @work League. Our team, UruBots, has been developing robots and projects for research and competitions in the last three years, attending robotics competitions in Uruguay and around the world. In this instance, we aim to participate and contribute to the RoboCup @Work category, hopefully making our debut in this prestigious competition. For that, we present an approach based on the Limo robot, whose main characteristic is its hybrid locomotion system with wheels and tracks, with some extras added by the team to complement the robot's functionalities. Overall, our approach allows the robot to efficiently and autonomously navigate a @work scenario, with the ability to manipulate objects, perform autonomous navigation, and engage in a simulated industrial environment.

--This paper describes the approach used by T eam UruBots for participation in the 2025 RoboCup Rescue Robot League competition. Our team aims to participate for the first time in this competition at RoboCup, using experience learned from previous competitions and research. We present our vehicle and our approach to tackle the task of detecting and finding victims in search and rescue environments. Our approach contains known topics in robotics, such as ROS, SLAM, Human Robot Interaction and segmentation and perception. Our proposed approach is open source, available to the RoboCup Rescue community, where we aim to learn and contribute to the league.