Tootfinder

ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations
Qiyuan Zeng, Chengmeng Li, Jude St. John, Zhongyi Zhou, Junjie Wen, Guorui Feng, Yichen Zhu, Yi Xu
https://arxiv.org/abs/2510.01607

ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations
We present ActiveUMI, a framework for a data collection system that transfers in-the-wild human demonstrations to robots capable of complex bimanual manipulation. ActiveUMI couples a portable VR teleoperation kit with sensorized controllers that mirror the robot's end-effectors, bridging human-robot kinematics via precise pose alignment. To ensure mobility and data quality, we introduce several key techniques, including immersive 3D model rendering, a self-contained wearable computer, and effic…

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Joshua R. Jandrell, Mitchell A. Cox
https://arxiv.org/abs/2508.19751 https://

Fourier Feature Networks for High-Fidelity Prediction of Perturbed Optical Fields
Modelling the effects of perturbations on optical fields often requires learning highly oscillatory complex-valued functions. Standard multi-layer perceptrons (MLPs) struggle with this task due to an inherent spectral bias, preventing them from fitting high-frequency sinusoids. To overcome this, we incorporate Fourier features - a set of predefined sinusoids dependent on the perturbation - as an additional network input. This reframes the learning problem from approximating a complex function t…

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Ankush Pratap Singh, Houwei Cao, Yong Liu
https://arxiv.org/abs/2510.09382 https://arxiv.org…

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Curriculum learning (CL) structures training from simple to complex samples, facilitating progressive learning. However, existing CL approaches for emotion recognition often rely on heuristic, data-driven, or model-based definitions of sample difficulty, neglecting the difficulty for human perception, a critical factor in subjective tasks like emotion recognition. We propose CHUCKLE (Crowdsourced Human Understanding Curriculum for Knowledge Led Emotion Recognition), a perception-driven CL frame…

Taste is used here as a research instrument, not a metaphor.
Each work translates a regional ecological transformation into a reproducible, time-based edible score
paired with a data-driven abstract drawing that represents perceived taste.
Together, an instructional certificate (containing the edible score),
a data-driven abstract drawing,
and a Bioregional Context Dossier form a single artwork
that can be performed, conserved, and collected.

Taste Cartographies Documenting Anthropocene Terroir Through Systematic Flavor Analysis — Beth Ames Altringer Eagle Portfolio
Taste Cartographies Documenting Anthropocene Terroir Through Systematic Flavor Analysis Project focus: Systematic translation of regional ecological systems...

Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection
Richard Kuhlmann, Jakob Wolfram, Boyang Sun, Jiaxu Xing, Davide Scaramuzza, Marc Pollefeys, Cesar Cadena
https://arxiv.org/abs/2509.17877

Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection
Autonomous inspection is a central problem in robotics, with applications ranging from industrial monitoring to search-and-rescue. Traditionally, inspection has often been reduced to navigation tasks, where the objective is to reach a predefined location while avoiding obstacles. However, this formulation captures only part of the real inspection problem. In real-world environments, the inspection targets may become visible well before their exact coordinates are reached, making further movemen…

Magnetic reconnection sustains the mass budget of the solar wind
Yajie Chen, Hardi Peter, Damien Przybylski, Haruhisa Iijima, Lakshmi Pradeep Chitta
https://arxiv.org/abs/2509.11692

Magnetic reconnection sustains the mass budget of the solar wind
The solar wind originates from regions of open magnetic fields on the Sun, but the relevant processes remain unsolved. We present a self-consistent numerical model of the source region of the wind, in which jets similar to those observed on the Sun naturally emerge due to magnetic reconnection between closed and open magnetic fields. In this process material is transferred from closed to open field lines and fed into the solar wind.We quantify the mass flux through the magnetic field connected …

The Two-Zone Temperature Distribution Model: Inferences on the Structure and Composition of Dusty Protoplanetary Disks
William Grimble, Joel Kastner, B. Sargent, Keivan Stassun
https://arxiv.org/abs/2510.12025

The Two-Zone Temperature Distribution Model: Inferences on the Structure and Composition of Dusty Protoplanetary Disks
In order to understand the mineralogy and structure of protoplanetary disks, it is important to analyze them from both an empirical spectrum-based perspective and a radiative transfer image-based perspective. In a prior paper, we set forth an empirical mineralogy mid-IR spectral model that conveyed spatial information and worked in tandem with a radiative transfer model, which formed the EaRTH Disk Model. In this article, we take the empirical portion of that model, the TZTD model, and refine i…

The Interplay between Utility and Risk in Portfolio Selection
Leonardo Baggiani, Martin Herdegen, Nazem Khan
https://arxiv.org/abs/2509.10351 https://arxiv…

The Interplay between Utility and Risk in Portfolio Selection
We revisit the problem of portfolio selection, where an investor maximizes utility subject to a risk constraint. Our framework is very general and accommodates a wide range of utility and risk functionals, including non-concave utilities such as S-shaped utilities from prospect theory and non-convex risk measures such as Value at Risk. Our main contribution is a novel and complete characterization of well-posedness for utility-risk portfolio selection in one period that takes the interplay betw…