<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Robot Papers</title><link>https://robotpapers.store</link><description>Latest robotics papers and engineering-ready research.</description><item><title><![CDATA[Collaborative Human-Agent Protocol (CHAP)]]></title><link>https://robotpapers.store/papers/collaborative-human-agent-protocol-chap</link><description><![CDATA[A robotics research paper on Collaborative Human-Agent Protocol (CHAP).]]></description></item><item><title><![CDATA[Perturbative Contrastive Physical Learning]]></title><link>https://robotpapers.store/papers/perturbative-contrastive-physical-learning</link><description><![CDATA[A robotics research paper on Perturbative Contrastive Physical Learning.]]></description></item><item><title><![CDATA[Difference-Aware Retrieval Policies for Imitation Learning]]></title><link>https://robotpapers.store/papers/difference-aware-retrieval-policies-for-imitation-learning</link><description><![CDATA[A robotics research paper on Difference-Aware Retrieval Policies for Imitation Learning.]]></description></item><item><title><![CDATA[Preserving Plasticity in Continual Learning via Dynamical Isometry]]></title><link>https://robotpapers.store/papers/preserving-plasticity-in-continual-learning-via-dynamical-isometry</link><description><![CDATA[A robotics research paper on Preserving Plasticity in Continual Learning via Dynamical Isometry.]]></description></item><item><title><![CDATA[iOSWorld: A Benchmark for Personally Intelligent Phone Agents]]></title><link>https://robotpapers.store/papers/iosworld-a-benchmark-for-personally-intelligent-phone-agents</link><description><![CDATA[A robotics research paper on iOSWorld: A Benchmark for Personally Intelligent Phone Agents.]]></description></item><item><title><![CDATA[Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan]]></title><link>https://robotpapers.store/papers/data-synthesis-and-parameter-efficient-fine-tuning-for-low-resource-nmt-a-case-study-on-q-eqchi-mayan</link><description><![CDATA[A robotics research paper on Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan.]]></description></item><item><title><![CDATA[Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model]]></title><link>https://robotpapers.store/papers/discovering-functionally-selective-brain-regions-with-a-deep-topographic-multimodal-model</link><description><![CDATA[A robotics research paper on Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model.]]></description></item><item><title><![CDATA[SemDINO: A DINOv3-Driven Network for Cross-Temporal Semantic Alignment in Change Detection]]></title><link>https://robotpapers.store/papers/semdino-a-dinov3-driven-network-for-cross-temporal-semantic-alignment-in-change-detection</link><description><![CDATA[A robotics research paper on SemDINO: A DINOv3-Driven Network for Cross-Temporal Semantic Alignment in Change Detection.]]></description></item><item><title><![CDATA[SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation]]></title><link>https://robotpapers.store/papers/siga-self-evolving-coding-agent-adapters-for-scientific-simulation</link><description><![CDATA[A robotics research paper on SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation.]]></description></item><item><title><![CDATA[AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning]]></title><link>https://robotpapers.store/papers/aetherock-an-arm-worn-robot-teaching-system-for-force-guided-vision-tactile-learning</link><description><![CDATA[A robotics research paper on AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning.]]></description></item><item><title><![CDATA[Who Earns the Safety? Intervention-Aware Quantum Predictive Control with Safety Attribution]]></title><link>https://robotpapers.store/papers/who-earns-the-safety-intervention-aware-quantum-predictive-control-with-safety-attribution</link><description><![CDATA[A robotics research paper on Who Earns the Safety? Intervention-Aware Quantum Predictive Control with Safety Attribution.]]></description></item><item><title><![CDATA[Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum]]></title><link>https://robotpapers.store/papers/zero-touch-predictive-orchestration-automating-time-series-models-for-the-cloud-edge-continuum</link><description><![CDATA[A robotics research paper on Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum.]]></description></item><item><title><![CDATA[POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction]]></title><link>https://robotpapers.store/papers/potatr-a-lightweight-image-to-graph-model-for-page-level-table-extraction</link><description><![CDATA[A robotics research paper on POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction.]]></description></item><item><title><![CDATA[End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout]]></title><link>https://robotpapers.store/papers/end-to-end-optimization-of-incoherent-imaging-for-classification-under-detector-limited-readout</link><description><![CDATA[A robotics research paper on End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout.]]></description></item><item><title><![CDATA[Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction]]></title><link>https://robotpapers.store/papers/beyond-spherical-harmonics-rethinking-appearance-models-for-radiance-reconstruction</link><description><![CDATA[A robotics research paper on Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction.]]></description></item><item><title><![CDATA[SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps]]></title><link>https://robotpapers.store/papers/synmandex-synthesizing-human-like-dexterous-grasps-from-synthetic-human-pre-grasps</link><description><![CDATA[A robotics research paper on SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps.]]></description></item><item><title><![CDATA[FASE: Fast Adaptive Semantic Entropy for Code Quality]]></title><link>https://robotpapers.store/papers/fase-fast-adaptive-semantic-entropy-for-code-quality</link><description><![CDATA[A robotics research paper on FASE: Fast Adaptive Semantic Entropy for Code Quality.]]></description></item><item><title><![CDATA[Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts]]></title><link>https://robotpapers.store/papers/bandits-for-efficient-experimentation-adapting-to-control-group-preferences-and-context-drifts</link><description><![CDATA[A robotics research paper on Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts.]]></description></item><item><title><![CDATA[Echo-Memory: A Controlled Study of Memory in Action World Models]]></title><link>https://robotpapers.store/papers/echo-memory-a-controlled-study-of-memory-in-action-world-models</link><description><![CDATA[A robotics research paper on Echo-Memory: A Controlled Study of Memory in Action World Models.]]></description></item><item><title><![CDATA[Topological Neural Operators]]></title><link>https://robotpapers.store/papers/topological-neural-operators</link><description><![CDATA[A robotics research paper on Topological Neural Operators.]]></description></item></channel></rss>