Nghiên cứu Papers
Bản tóm tắt dễ hiểu về các nghiên cứu robot mới nhất — robot hình người, robot bốn chân, tự động hóa công nghiệp và AI.
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ACID Framework Boosts Robotic Planning With Action Consistency Check

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LIME Teaches Robots Where to Look Based on Language Intent

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WorldSample Boosts Real Robot Learning with World Model Data Augmentation

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Task-Agnostic Pretraining: Teaching Robots to Move Before Teaching Tasks

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Differentiable Simulation Trains Drones to Intercept Intruders Using Only Direction

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QuadRocket Testbed Uses a Drone to Control a Rocket-Like Flying Pendulum

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Controllable Traffic Agents Learn Steering Behaviors From Latent Variables

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Embodied.cpp Enables Efficient C++ Inference for Embodied AI on Heterogeneous Robots

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Visual-Tactile World Action Model for Contact-Rich Manipulation

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Sensorless Teleoperation Uses Inverse Dynamics for Transparent Control

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GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics

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FurnitureVLA: Teaching Robots to Assemble IKEA Furniture Using Vision-Language-Action Models

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PrimeU: Humanoid Robot That Learns Tasks by Watching Human Videos

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Freeform Preferences Help Robots Learn From Human Feedback Without Rigid Ratings

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Disentangled Video World Model Speeds Robotic Planning Without Sacrificing Quality

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Sequential Planning via Anchored Robotic Keypoints: Training-Free Robot Manipulation

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GROW²: Teaching Robots to Select Tools and Find Their Functional Regions

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Vision-Language-Kinematics: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed 3D Scenes

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