机器人
66 篇 · 资讯流Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents
Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization
原文 ↗DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation
Learning robust manipulation policies for diverse, long-horizon tasks from limited demonstrations remains a fundamental
原文 ↗Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching
Autoregressive Vision-Language-Action (VLA) models offer a scalable path to robot learning, yet existing action tokenize
原文 ↗Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation
This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories a
原文 ↗From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world robot data. While recent approac
原文 ↗JRDB-AVR: An Active Visual Reasoning Benchmark for Embodied Agents in Real-World Environments
In complex embodied visual reasoning scenarios, an agent often has only a limited field of view, and the evidence needed
原文 ↗M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera--LiDAR Novel View Synthesis
Robotic novel view synthesis (NVS) must recover both visual appearance and metric 3D structure, yet most generative NVS
原文 ↗Retrieve-to-Localize: Bridging Large Language Models and LiDAR Geometry for Spatial Grounding
LiDAR provides precise geometric information for spatial perception tasks such as object detection in autonomous driving
原文 ↗Watch, Recall, Act: Always-On Robots in Concurrent Embodied Streams
An always-on robot faces an endless stream that never resets: instructions arrive and lapse, the scene changes, and its
原文 ↗PointCast: One World Model for Rigid, Articulated, and Deformable Object Manipulation
World models are useful for robotic manipulation because robots can predict how actions change the states of objects bef
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