Tootfinder

Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors
Yuying Zhang, Joni Pajarinen
https://arxiv.org/abs/2508.13151 https://

Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors
Mobile manipulation in dynamic environments is challenging due to movable obstacles blocking the robot's path. Traditional methods, which treat navigation and manipulation as separate tasks, often fail in such 'manipulate-to-navigate' scenarios, as obstacles must be removed before navigation. In these cases, active interaction with the environment is required to clear obstacles while ensuring sufficient space for movement. To address the manipulate-to-navigate problem, we propose a reinforcemen…

Joint Scheduling and Multiflow Maximization in Wireless Networks
Yanxiao Liu, Shenghao Yang, Cheuk Ting Li
https://arxiv.org/abs/2509.14582 https://arxiv.o…

Joint Scheduling and Multiflow Maximization in Wireless Networks
Towards the development of 6G mobile networks, it is promising to integrate a large number of devices from multi-dimensional platforms, and it is crucial to have a solid understanding of the theoretical limits of large-scale networks. We revisit a fundamental problem at the heart of network communication theory: the maximum multiflow (MMF) problem in multi-hop networks, with network coding performed at intermediate nodes. To derive the exact-optimal solution to the MMF problem (as opposed to ap…

FSF announces Librephone project https://www.fsf.org/news/librephone-project
"The Free Software Foundation announced its project to bring mobile phone freedom to users. "Librephone" is an initiative to reverse-engineer obstacles preventing mobile phone freedom until its goal is…

"Dazu kommt, dass kein Autofahrer weiß, wo die Tempo-30-Zone endet"
Na doch, steht direkt drunter. Als Autofahrender wird man doch wohl 150 Meter abschätzen können. Notfalls muss man zur Sicherheit eben etwas länger 30 km/h fahren.
https://www.

Raser-Falle in Dresden falsch aufgestellt: Anwalt lässt das Rathaus abblitzen
Zwei neue mobile "Superblitzer" erfassen in Dresden Raser. Reichlich Bußgeld fürs Stadtsäckel. Doch Obacht! Das Rathaus blitzt nicht immer korrekt.

Real-Time Obstacle Avoidance for a Mobile Robot Using CNN-Based Sensor Fusion
Lamiaa H. Zain, Raafat E. Shalaby
https://arxiv.org/abs/2509.08095 https://ar…

Real-Time Obstacle Avoidance for a Mobile Robot Using CNN-Based Sensor Fusion
Obstacle avoidance is a critical component of the navigation stack required for mobile robots to operate effectively in complex and unknown environments. In this research, three end-to-end Convolutional Neural Networks (CNNs) were trained and evaluated offline and deployed on a differential-drive mobile robot for real-time obstacle avoidance to generate low-level steering commands from synchronized color and depth images acquired by an Intel RealSense D415 RGB-D camera in diverse environments. …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- Real-Time Obstacle Avoidance for a Mobile Robot Using CNN-Based Sensor Fusion
Lamiaa H. Zain, Raafat E. Shalaby

Five principles to protect the human knowledge ecosystem 3/5

Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications
Chanwoo Kim, Jihwan Yoon, Hyeonseong Kim, Taemoon Jeong, Changwoo Yoo, Seungbeen Lee, Soohwan Byeon, Hoon Chung, Matthew Pan, Jean Oh, Kyungjae Lee, Sungjoon Choi
https://arxiv.org/abs/2510.12215

Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications
Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance of adaptability and safety. To this end, we develop a framework that learns a density-based reward from positive and negative demonstrations and augments it with rule-based objectives for obstacle a…

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
Faduo Liang, Yunfeng Yang, Shi-Lu Dai
https://arxiv.org/abs/2510.02885

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently me…

A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
Dennis Benders, Laura Ferranti, Johannes K\"ohler
https://arxiv.org/abs/2507.17856

A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
Designing a Model Predictive Control (MPC) scheme that enables a mobile robot to safely navigate through an obstacle-filled environment is a complicated yet essential task in robotics. In this technical report, safety refers to ensuring that the robot respects state and input constraints while avoiding collisions with obstacles despite the presence of disturbances and measurement noise. This report offers a step-by-step approach to implementing Nonlinear Model Predictive Control (NMPC) schemes …