Tootfinder

Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
Vijja Wichitwechkarn, Emlyn Williams, Charles Fox, Ruchi Choudhary
https://arxiv.org/abs/2509.24972

Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
Recent advances in one-shot imitation learning have enabled robots to acquire new manipulation skills from a single human demonstration. While existing methods achieve strong performance on single-step tasks, they remain limited in their ability to handle long-horizon, multi-step tasks without additional model training or manual annotation. We propose a method that can be applied to this setting provided a single demonstration without additional model training or manual annotation. We evaluated…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
Anirban Chatterjee, Sayantan Choudhury, Rohan Hore
https://arxiv.org/abs/2509.25507 https://arxi…

One-shot Conditional Sampling: MMD meets Nearest Neighbors
How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference, and conditional distribution modeling in complex data settings. In such settings, compared with unconditional sampling, additional feature information can be leveraged to enable mo…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/8]:
- One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo
Hao-Nan Shi, Ting-Ji Huang, Lu Han, De-Chuan Zhan, Han-Jia Ye

Needed to build something quite weird that involves NFC tags and mainly Notion DB as storage and UI
Didn’t feel like learning Notion cos I am not a fan.
Worked with ChatGPT to make a spec and prompt this was useful while doing that totally changed my mind to something better
Gave it to codex and it one-shot built it perfectly in 20 minutes without interaction, worked first time once I fixed a mistake I made in Notion.
Lots of issues with AI but it’s certainly moving…

Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Zewen Yang, Xiaobing Dai, Dongfa Zhang, Yu Li, Ziyang Meng, Bingkun Huang, Hamid Sadeghian, Sami Haddadin
https://arxiv.org/abs/2509.14040

Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize across coordinate transformations. In this paper, we propose Prompt2Auto, a geometry-invariant one-shot Gaussian process (GeoGP) learning framework that enables robots to perform human-guided automated control from a single motion prompt. A dataset-construction strategy based on coordinate transformations is introduced tha…

Hint: hierarchical inter-frame correlation for one-shot point cloud sequence compression
Yuchen Gao, Qi Zhang
https://arxiv.org/abs/2509.14859 https://arxi…

Hint: hierarchical inter-frame correlation for one-shot point cloud sequence compression
Deep learning has demonstrated strong capability in compressing point clouds. Within this area, entropy modeling for lossless compression is widely investigated. However, most methods rely solely on parent orsibling contexts and level-wise autoregression, which suffers from decoding latency on the order of 10 to 100 seconds. We propose HINT, a method that integrates temporal and spatial correlation for sequential point cloud compression. Specifically, it first uses a two stage temporal feature …

VLBiMan: Vision-Language Anchored One-Shot Demonstration Enables Generalizable Robotic Bimanual Manipulation
Huayi Zhou, Kui Jia
https://arxiv.org/abs/2509.21723 https://…

VLBiMan: Vision-Language Anchored One-Shot Demonstration Enables Generalizable Robotic Bimanual Manipulation
Achieving generalizable bimanual manipulation requires systems that can learn efficiently from minimal human input while adapting to real-world uncertainties and diverse embodiments. Existing approaches face a dilemma: imitation policy learning demands extensive demonstrations to cover task variations, while modular methods often lack flexibility in dynamic scenes. We introduce VLBiMan, a framework that derives reusable skills from a single human example through task-aware decomposition, preser…