计算机视觉
21 篇 · 资讯流TAPESTRY: A Single Backbone for Video Generation and Robot Control
Pretrained video generation models encode rich priors about physical interactions, but leveraging them for robot control
原文 ↗Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success
Robotic foundation models are typically evaluated through costly closed-loop rollouts, which motivates cheap offline pro
原文 ↗Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use
Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-us
原文 ↗UniWALM: Unified World Action Language Model for Embodied Robot Control
Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a
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 ↗ChunkAct: Action Chunking for Agentic Robotics
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, moti
原文 ↗UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, res
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
原文 ↗RARA: Robust Policy Fine-Tuning through Retrieval-Anchored Representation Alignment for Robotic Manipulation
Pre-trained robot policies, such as Vision-Language-Action (VLA) models, provide a strong foundation for generalist robo
原文 ↗DVD: Dynamic Vector Decoding for Efficient MLLM-based Perception
Multimodal large language models have made remarkable progress in bridging vision and language, facilitating various per
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