Tootfinder

HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton
Rui Zhong, Yizhe Sun, Junjie Wen, Jinming Li, Chuang Cheng, Wei Dai, Zhiwen Zeng, Huimin Lu, Yichen Zhu, Yi Xu
https://arxiv.org/abs/2510.03022

HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton
A significant bottleneck in humanoid policy learning is the acquisition of large-scale, diverse datasets, as collecting reliable real-world data remains both difficult and cost-prohibitive. To address this limitation, we introduce HumanoidExo, a novel system that transfers human motion to whole-body humanoid data. HumanoidExo offers a high-efficiency solution that minimizes the embodiment gap between the human demonstrator and the robot, thereby tackling the scarcity of whole-body humanoid data…

As SBOMs slowly progress at the federal level and in enterprises, the rise of AI coding assistants is fueling optimistic—and, some experts argue, “kind of insane”—claims about a future with vulnerability-free software.
Check out my latest CyberScoop piece.
Many thanks to @…, @…

The slow rise of SBOMs meets the rapid advance of AI
Despite progress from CISA and global regulators, SBOM adoption in the private sector remains slow as experts debate if AI-driven coding will improve or undermine software security and transparency.

Der letzte #Mangold wurde Mitte Oktober geerntet und dann kamen gleich zwei #Grünkohlpflanzen in den Kasten. Ich musste noch welche vereinzeln. Die Saat war gut aufgegangen. Allerdings bekamen die letzten ausgesetzten Pflanzen nicht mehr viel

Machine Learning Frameworks for Large-Scale Radio Surveys: A Summary of Recent Studies
Nikhel Gupta
https://arxiv.org/abs/2510.11145 https://arxiv.org/pdf/…

Machine Learning Frameworks for Large-Scale Radio Surveys: A Summary of Recent Studies
The rapid growth of large-scale radio surveys, generating over 100 petabytes of data annually, has created a pressing need for automated data analysis methods. Recent research has explored the application of machine learning techniques to address the challenges associated with detecting and classifying radio galaxies, as well as discovering peculiar radio sources. This paper provides an overview of our investigations with the Evolutionary Map of the Universe (EMU) survey, detailing the methodol…

Placeit! A Framework for Learning Robot Object Placement Skills
Amina Ferrad, Johann Huber, Fran\c{c}ois H\'el\'enon, Julien Gleyze, Mahdi Khoramshahi, St\'ephane Doncieux
https://arxiv.org/abs/2510.09267

Placeit! A Framework for Learning Robot Object Placement Skills
Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placei…