Tootfinder

Compose by Focus: Scene Graph-based Atomic Skills
Han Qi, Changhe Chen, Heng Yang
https://arxiv.org/abs/2509.16053 https://arxiv.org/pdf/2509.16053

Compose by Focus: Scene Graph-based Atomic Skills
A key requirement for generalist robots is compositional generalization - the ability to combine atomic skills to solve complex, long-horizon tasks. While prior work has primarily focused on synthesizing a planner that sequences pre-learned skills, robust execution of the individual skills themselves remains challenging, as visuomotor policies often fail under distribution shifts induced by scene composition. To address this, we introduce a scene graph-based representation that focuses on task-…

Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment
Mateusz Slysz, Piotr Rydlichowski, Krzysztof Kurowski, Omar Bacarezza, Esperanza Cuenca Gomez, Zohim Chandani, Bettina Heim, Pradnya Khalate, William R. Clements, James Fletcher
https://arxiv.org/abs/2508.16297

Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment
Achieving a practical quantum advantage for near-term applications is widely expected to rely on hybrid classical-quantum algorithms. To deliver this practical advantage to users, high performance computing (HPC) centers need to provide a suitable software and hardware stack that supports algorithms of this type. In this paper, we describe the world's first implementation of a classical-quantum environment in an HPC center that allows multiple users to execute hybrid algorithms on multiple quan…

Autiverse: Eliciting Autistic Adolescents' Daily Narratives through AI-guided Multimodal Journaling
Migyeong Yang, Kyungah Lee, Jinyoung Han, SoHyun Park, Young-Ho Kim
https://arxiv.org/abs/2509.17466

Autiverse: Eliciting Autistic Adolescents' Daily Narratives through AI-guided Multimodal Journaling
Journaling can potentially serve as an effective method for autistic adolescents to improve narrative skills. However, its text-centric nature and high executive functioning demands present barriers to practice. We present Autiverse, an AI-guided multimodal journaling app for tablets that scaffolds storytelling through conversational prompts and visual supports. Autiverse elicits key details through a stepwise dialogue with peer-like, customizable AI and composes them into an editable four-pane…

MOOM: Maintenance, Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues
Weishu Chen, Jinyi Tang, Zhouhui Hou, Shihao Han, Mingjie Zhan, Zhiyuan Huang, Delong Liu, Jiawei Guo, Zhicheng Zhao, Fei Su
https://arxiv.org/abs/2509.11860

MOOM: Maintenance, Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues
Memory extraction is crucial for maintaining coherent ultra-long dialogues in human-robot role-playing scenarios. However, existing methods often exhibit uncontrolled memory growth. To address this, we propose MOOM, the first dual-branch memory plugin that leverages literary theory by modeling plot development and character portrayal as core storytelling elements. Specifically, one branch summarizes plot conflicts across multiple time scales, while the other extracts the user's character profil…

Activity-driven clustering of jamming run-and-tumble particles: Exact three-body steady state by dynamical symmetry
Leo Hahn, Arnaud Guillin, Manon Michel
https://arxiv.org/abs/2509.08945

Activity-driven clustering of jamming run-and-tumble particles: Exact three-body steady state by dynamical symmetry
We exactly resolve the three-particle steady state of run-and-tumble particles with jamming interactions, providing the first microscopic description beyond two bodies. The invariant measure, derived via a piecewise-deterministic Markov process description and symmetry principles, reveals persistent, separated, and diffusive regimes. A cascade of scales in the activity parameter organizes the structural weights, showing the separated phase dominates at finite activity, while non-uniformity play…

MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap
Tianyu Wu, Xudong Han, Haoran Sun, Zishang Zhang, Bangchao Huang, Chaoyang Song, Fang Wan
https://arxiv.org/abs/2509.19169

MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap
The transfer of manipulation skills from human demonstration to robotic execution is often hindered by a "domain gap" in sensing and morphology. This paper introduces MagiClaw, a versatile two-finger end-effector designed to bridge this gap. MagiClaw functions interchangeably as both a handheld tool for intuitive data collection and a robotic end-effector for policy deployment, ensuring hardware consistency and reliability. Each finger incorporates a Soft Polyhedral Network (SPN) with an embedd…

Evidence for Supermassive Black Hole Binaries
Martin G. H. Krause, Martin A. Bourne, Silke Britzen, Adi Foord, Jenny E. Greene, Melanie Habouzit, Maya A. Horton, Lucio Mayer, Hannah Middleton, Rebecca Nealon, Julia M. Sisk-Reyn\'es, Christopher S. Reynolds, Debora Sijacki
https://arxiv.org/abs/2510.07534

Evidence for Supermassive Black Hole Binaries
We review the state of the evidence for the existence and observational appearance of supermassive black hole binaries. Such objects are expected from standard hierarchical galaxy evolution to form after two galaxies, each containing a supermassive black hole, have merged, in the centre of the merger remnant. A complex interaction is predicted to take place with stars and gas in the host galaxy, leading to observable signatures in weakly as well as actively accreting phases. Direct observationa…

Implicit Kinodynamic Motion Retargeting for Human-to-humanoid Imitation Learning
Xingyu Chen, Hanyu Wu, Sikai Wu, Mingliang Zhou, Diyun Xiang, Haodong Zhang
https://arxiv.org/abs/2509.15443

Implicit Kinodynamic Motion Retargeting for Human-to-humanoid Imitation Learning
Human-to-humanoid imitation learning aims to learn a humanoid whole-body controller from human motion. Motion retargeting is a crucial step in enabling robots to acquire reference trajectories when exploring locomotion skills. However, current methods focus on motion retargeting frame by frame, which lacks scalability. Could we directly convert large-scale human motion into robot-executable motion through a more efficient approach? To address this issue, we propose Implicit Kinodynamic Motion R…

Trajectory Conditioned Cross-embodiment Skill Transfer
YuHang Tang, Yixuan Lou, Pengfei Han, Haoming Song, Xinyi Ye, Dong Wang, Bin Zhao
https://arxiv.org/abs/2510.07773 https:/…

Trajectory Conditioned Cross-embodiment Skill Transfer
Learning manipulation skills from human demonstration videos presents a promising yet challenging problem, primarily due to the significant embodiment gap between human body and robot manipulators. Existing methods rely on paired datasets or hand-crafted rewards, which limit scalability and generalization. We propose TrajSkill, a framework for Trajectory Conditioned Cross-embodiment Skill Transfer, enabling robots to acquire manipulation skills directly from human demonstration videos. Our key …