Tagged articles

Vision-Language Navigation

8 articles · Page 1 of 1

Nov 2, 2026 · Artificial Intelligence

RSI-Nav: Self-Improving Robot Navigation with Unlabeled Trajectories, +17.7% Success

Fudan University's RSI-Nav uses a single Qwen3.5-9B model to both follow language instructions and generate them from unlabeled trajectories, then verifies generated instructions by executing them in simulation, filtering successful ones to iteratively improve navigation performance, achieving 68.6% and 72.4% success on R2R-CE and RxR-CE benchmarks, a 15.7 and 17.7 point gain, and demonstrates real-world deployment on a DEEP Robotics M20 quadruped.

Data-Centric AIInstruction GenerationQwen3.5-9B

0 likes · 14 min read

RSI-Nav: Self-Improving Robot Navigation with Unlabeled Trajectories, +17.7% Success

Machine Heart

Oct 29, 2026 · Artificial Intelligence

SimpleVLN: Native MLLMs Achieve Navigation SOTA Without Extra Modules

Fudan and NTU researchers propose SimpleVLN, a vision-language navigation framework that unlocks state-of-the-art performance by using native multimodal large language models without architectural changes, combining observation selection, action chunking, and a three-stage training pipeline for real-world robot deployment.

Action ChunkingDAggerMLLM

0 likes · 10 min read

SimpleVLN: Native MLLMs Achieve Navigation SOTA Without Extra Modules

SmartApe AI

Oct 17, 2026 · Artificial Intelligence

HA-VLN 2.0: Benchmarking Socially-Aware Navigation in Dynamic Human Environments

Researchers from UW, CMU, NUS, and Microsoft Research introduce HA-VLN 2.0, a benchmark for vision-language navigation that incorporates dynamic human movements, multi-person interactions, and social constraints across discrete and continuous environments, featuring the HAPS 2.0 dataset with 172 activity types and HA-R2R with 16,844 enriched instructions, plus baselines and real-robot validation on a Unitree Go2.

Dynamic EnvironmentsHA-R2RHAPS 2.0

0 likes · 9 min read

HA-VLN 2.0: Benchmarking Socially-Aware Navigation in Dynamic Human Environments

Machine Learning Algorithms & Natural Language Processing

Sep 12, 2026 · Artificial Intelligence

Robots Retracing LLMs' Scaling Path: LightNav-0 & Light REACT Explained

Light Source Innovation, founded by ex-OpenAI RLHF expert Jiang Xu, releases LightNav-0 for zero-shot cross-morphology navigation and Light REACT for whole-body resilience control, applying LLM-style scalable pre-training, alignment, and deployment paradigms to embodied AI with sim-to-real synthetic data and preference-aligned RL.

RLHFScaling LawsSim-to-Real

0 likes · 14 min read

Robots Retracing LLMs' Scaling Path: LightNav-0 & Light REACT Explained

Machine Heart

Sep 10, 2026 · Artificial Intelligence

AgentVLN: VLM-as-Brain Architecture for Agentic Robot Navigation at ECCV 2026

AgentVLN introduces a VLM-as-Brain architecture for vision-language navigation, using cross-space representation mapping to translate 3D paths into 2D visual prompts, context-driven self-correction for error recovery, and query-driven perceptual chain-of-thought for active information gathering, achieving state-of-the-art results on R2R-CE and RxR-CE benchmarks with a 3B model deployable on Jetson edge devices.

AgentVLNCross-Space Representation MappingECCV 2026

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AgentVLN: VLM-as-Brain Architecture for Agentic Robot Navigation at ECCV 2026

Machine Heart

Apr 30, 2026 · Artificial Intelligence

Can Internet Videos Replace 3D Annotations? Introducing SceneVerse++ – the Largest Real‑World 3D Scene Dataset

The BIGAI team presents SceneVerse++, a massive real‑world indoor 3D scene dataset built from unlabelled internet videos via an automated pipeline, and demonstrates substantial zero‑shot and fine‑tuned performance gains on 3D detection, spatial VQA, and vision‑language navigation tasks.

3D Scene UnderstandingSceneVerse++Vision-Language Navigation

0 likes · 18 min read

Can Internet Videos Replace 3D Annotations? Introducing SceneVerse++ – the Largest Real‑World 3D Scene Dataset

Amap Tech

Oct 4, 2025 · Artificial Intelligence

How JanusVLN Redefines Vision‑Language Navigation with Dual Implicit Memory

JanusVLN presents a groundbreaking Vision‑and‑Language Navigation framework that decouples semantic understanding from spatial geometry using dual implicit memory, eliminates explicit memory overhead, achieves state‑of‑the‑art performance with only RGB video input, and dramatically improves efficiency and generalization across VLN benchmarks.

3D spatial reasoningDual Implicit MemoryVision-Language Navigation

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How JanusVLN Redefines Vision‑Language Navigation with Dual Implicit Memory

Meituan Technology Team

Jun 15, 2023 · Artificial Intelligence

Meituan Technical Team's 8 CVPR 2023 Papers: Overview and Insights

This article reviews eight CVPR 2023 papers selected by Meituan’s technology team, covering self‑supervised learning, domain adaptation, federated learning, object detection, 3D reconstruction, GAN‑based pre‑training, RGB‑T tracking, vision‑language navigation, and visual‑textual layout generation, highlighting each work’s methodology, experiments, and reported performance gains.

3D Object DetectionCVPR 2023Computer Vision

0 likes · 15 min read

Meituan Technical Team's 8 CVPR 2023 Papers: Overview and Insights