机器人
51 篇 · 资讯流Frontier LLMs Enter the World of Robotics: A Cross-Model Evaluation on RoboColiseum
Recent studies have explored the potential of multimodal large language models for robot control, yet the models' embodi
原文 ↗CovNav: Conformal Prediction-Guided Adaptive Inference for Object Goal Navigation
In object goal navigation (ObjectNav), an embodied agent must find a target object (e.g., "find a bed") in a previously
原文 ↗Physical Words: A Vocabulary for Structuring Latent Reasoning in Robot Manipulation
Multimodal reasoning provides robotic foundation models with predictive context needed for reliable action generation. H
原文 ↗GPT-6 Astra Is a Native Visuo-Tactile Policy for Contact-Rich Manipulation
Recent advances in multimodal models have enabled GPT-6 Astra to perform robotic manipulation through in-context learnin
原文 ↗ForagerSim: Evaluating Embodied Map-Based Reasoning in Large-Scale Dynamic Worlds
Animals build cognitive maps of vast environments, use them to perform map-based navigation, and might even be reusing t
原文 ↗Unifying Embodied Foundation Models via a Model-to-Policy Formulation
Vision-language-action (VLA) models and world-action models (WAMs) have emerged as promising approaches to robot learnin
原文 ↗Auditable and Deployable Multi-Agent Motion Planning via Posetal Games from Legal Guidelines
When robots are deployed in the real-world, they must interact with other agents and accomplish their own goals while ad
原文 ↗Tactile-Guided Robot Learning: From Representation Learning to Foundation Models
Tactile sensing is becoming central to robust, adaptive, and dexterous robotic manipulation. Beyond its traditional role
原文 ↗Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion
Collaborative planning under operational constraints is essential for heterogeneous robot teams in large-scale missions.
原文 ↗Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence
World models have emerged as a promising paradigm for embodied AI, yet existing systems often remain fragmented in the m
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