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Vision-Language Navigation
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Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.
<span style="color:grey; opacity: 0.6">( Image credit: Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
LaViPlan : Language-Guided Visual Path Planning with RLVR
SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models
Vision Language Action Models in Robotic Manipulation: A Systematic Review
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
A Survey on Vision-Language-Action Models for Autonomous Driving
Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
WorldVLA: Towards Autoregressive Action World Model
CronusVLA: Transferring Latent Motion Across Time for Multi-Frame Prediction in Manipulation
Unified Vision-Language-Action Model
VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models
RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning
CapsDT: Diffusion-Transformer for Capsule Robot Manipulation
A Comprehensive Survey on Continual Learning in Generative Models
Block-wise Adaptive Caching for Accelerating Diffusion Policy
ROSA: Harnessing Robot States for Vision-Language and Action Alignment
LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding
From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models
SAFE: Multitask Failure Detection for Vision-Language-Action Models
EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models
TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
Hybrid Reasoning for Perception, Explanation, and Autonomous Action in Manufacturing
Generating Vision-Language Navigation Instructions Incorporated Fine-Grained Alignment Annotations
FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
An Open-Source Software Toolkit & Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models
Real-Time Execution of Action Chunking Flow Policies
BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation
BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models
Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion Models in a Vision-Language-Action Framework
Robotic Policy Learning via Human-assisted Action Preference Optimization
Active Test-time Vision-Language Navigation
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