Tootfinder

DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits
Lantao Li, Kang Yang, Rui Song, Chen Sun
https://arxiv.org/abs/2509.24903 https://

DRCP: Diffusion on Reinforced Cooperative Perception for Perceiving Beyond Limits
Cooperative perception enabled by Vehicle-to-Everything communication has shown great promise in enhancing situational awareness for autonomous vehicles and other mobile robotic platforms. Despite recent advances in perception backbones and multi-agent fusion, real-world deployments remain challenged by hard detection cases, exemplified by partial detections and noise accumulation which limit downstream detection accuracy. This work presents Diffusion on Reinforced Cooperative Perception (DRCP)…

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Hinrich Sch\"utze, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.19828

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external memory bank, yet most existing pipelines are static and heuristic-driven, lacking any learned mechanism for deciding what to store, update, or retrieve. We present Memory-R1, a reinforcement learning …

Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
Liding Zhang, Kuanqi Cai, Zhenshan Bing, Chaoqun Wang, Alois Knoll
https://arxiv.org/abs/2508.20871

Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort …

Important & informative 🧵re #indigenous & #uncontactedpeople
https://masto.ai/@Nonilex/115446627734

Important & informative 🧵re #indigenous & #uncontactedpeople
https://masto.ai/@Nonilex/115446627734

Cowboys deliver best defensive effort of the season vs. the Commanders -- and reinforcements are on the way

https://www.cbssports.com/nfl/news/cowboys-injury-updates-…

Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI
Hannah Selder, Florian Fischer, Per Ola Kristensson, Arthur Fleig
https://arxiv.org/abs/2508.15727

Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI
Designing effective reward functions is critical for reinforcement learning-based biomechanical simulations, yet HCI researchers and practitioners often waste (computation) time with unintuitive trial-and-error tuning. This paper demystifies reward function design by systematically analyzing the impact of effort minimization, task completion bonuses, and target proximity incentives on typical HCI tasks such as pointing, tracking, and choice reaction. We show that proximity incentives are essent…

"Belize project seeks out heat-resilient corals to protect its reefs"
#Belize #CoralReef #Environment

Belize project seeks out heat-resilient corals to protect its reefs
HOPKINS, Belize — Heaters, pumps and computerized meters rigged to an everyday picnic icebox may seem like a high school science project. But this heat stress tank may hold the key to finding the most heat-resilient corals in Belize’s waters. As climate change and other human-caused stressors continue to push these rainforests of the sea […]

Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Rushuai Yang, Hangxing Wei, Ran Zhang, Zhiyuan Feng, Xiaoyu Chen, Tong Li, Chuheng Zhang, Li Zhao, Jiang Bian, Xiu Su, Yi Chen
https://arxiv.org/abs/2509.19752

Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Vision-language-action (VLA) models have shown strong generalization across tasks and embodiments; however, their reliance on large-scale human demonstrations limits their scalability owing to the cost and effort of manual data collection. Reinforcement learning (RL) offers a potential alternative to generate demonstrations autonomously, yet conventional RL algorithms often struggle on long-horizon manipulation tasks with sparse rewards. In this paper, we propose a modified diffusion policy opt…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Yongliang Wang, Hamidreza Kasaei
https://arxiv.org/abs/2509.17381 https://…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Generating obstacle-free trajectories for robotic manipulators in unstructured and cluttered environments remains a significant challenge. Existing motion planning methods often require additional computational effort to generate the final trajectory by solving kinematic or dynamic equations. This paper highlights the strong potential of model-free reinforcement learning methods over model-based approaches for obstacle-free trajectory planning in joint space. We propose a fast trajectory planni…