Tootfinder

Empowering Multi-Robot Cooperation via Sequential World Models
Zijie Zhao, Honglei Guo, Shengqian Chen, Kaixuan Xu, Bo Jiang, Yuanheng Zhu, Dongbin Zhao
https://arxiv.org/abs/2509.13095

Empowering Multi-Robot Cooperation via Sequential World Models
Model-based reinforcement learning (MBRL) has shown significant potential in robotics due to its high sample efficiency and planning capability. However, extending MBRL to multi-robot cooperation remains challenging due to the complexity of joint dynamics. To address this, we propose the Sequential World Model (SeqWM), a novel framework that integrates the sequential paradigm into model-based multi-agent reinforcement learning. SeqWM employs independent, sequentially structured agent-wise world…

JD Vance made jokes about this woman's husband being dead.
https://bsky.app/profile/mpaarlberg.bsky.social/post/3lzydxbicas2n

Michael Paarlberg (@mpaarlberg.bsky.social)
I think the Trump admin assumed no one would try to find out who the people on the boats they blew up really were

Context-Aware Model-Based Reinforcement Learning for Autonomous Racing
Emran Yasser Moustafa, Ivana Dusparic
https://arxiv.org/abs/2510.11501 https://arxiv…

Context-Aware Model-Based Reinforcement Learning for Autonomous Racing
Autonomous vehicles have shown promising potential to be a groundbreaking technology for improving the safety of road users. For these vehicles, as well as many other safety-critical robotic technologies, to be deployed in real-world applications, we require algorithms that can generalize well to unseen scenarios and data. Model-based reinforcement learning algorithms (MBRL) have demonstrated state-of-the-art performance and data efficiency across a diverse set of domains. However, these algori…

JD Vance made jokes about this woman's husband being dead.
https://bsky.app/profile/mpaarlberg.bsky.social/post/3lzydxbicas2n

Learning to Walk with Less: a Dyna-Style Approach to Quadrupedal Locomotion
Francisco Affonso, Felipe Andrade G. Tommaselli, Juliano Negri, Vivian S. Medeiros, Mateus V. Gasparino, Girish Chowdhary, Marcelo Becker
https://arxiv.org/abs/2509.06296

Learning to Walk with Less: a Dyna-Style Approach to Quadrupedal Locomotion
Traditional RL-based locomotion controllers often suffer from low data efficiency, requiring extensive interaction to achieve robust performance. We present a model-based reinforcement learning (MBRL) framework that improves sample efficiency for quadrupedal locomotion by appending synthetic data to the end of standard rollouts in PPO-based controllers, following the Dyna-Style paradigm. A predictive model, trained alongside the policy, generates short-horizon synthetic transitions that are gra…

On the generation of free-surface waves by instabilities in quadratic shear flows
Harishankar Muppirala, Ramana Patibandla, Anubhab Roy
https://arxiv.org/abs/2508.20251 https://…

On the generation of free-surface waves by instabilities in quadratic shear flows
This paper investigates the generation of free-surface waves in a liquid layer driven by linear instabilities in Couette-Poiseuille (quadratic) shear flows. The base velocity profiles are characterized by a curvature parameter, and two-dimensional viscous and inviscid perturbations are analyzed across a wide parameter space of curvature, wavenumber, and Reynolds number, for fixed Froude and Bond numbers. In the inviscid limit, analytical solutions of the Rayleigh equation reveal that velocity p…

🇺🇦 Auf radioeins läuft...
Empire Of The Sun:
🎵 We Are The People
#NowPlaying #EmpireOfTheSun
https://edicionesyremixesdjalesvanhotmailcom.bandcamp.com/album/empire-of-the-sun-we-are-the-people
https://open.spotify.com/track/5NzeaGdmieI4pRlNLv5p7L

🇺🇦 #NowPlaying on #BBC6Music's #The6MixWithMaryAnneHobbs
MMM:
🎵 Casio Dub
#MMM
https://mmmberlin.bandcamp.com/track/casio-dub

DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao, Weipu Zhang, Jian Sun, Gao Huang, Jie Chen, Gang Wang
https://arxiv.org/abs/2509.24804 …

DyMoDreamer: World Modeling with Dynamic Modulation
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-based reinforcement learning (MBRL) mitigates this by building world models that simulate environmental dynamics and generate synthetic experience, improving sample efficiency. However, conventional world models process observations holistically, failing to decouple dynamic objects and temporal features from static backg…

🇺🇦 #NowPlaying on BBCRadio3's #BBCProms
Andršs Schiff & Robert Schumann:
🎵 Arabeske in C major, Op 18
#AndrásSchiff #RobertSchumann
https://open.spotify.com/track/5MUVv3BR6lv8MFPDTtdUQI