Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System research paper by Alibaba (Qwen), 2026
Alibaba (Qwen) · Foundation models · Jun 18, 2026 · 23 citations · 29 upvotes · unverified
What it shows
Qwen-RobotNav is a scalable navigation model with a parameterized interface enabling dynamic task modes and observation parameters, achieving state-of-the-art performance through multi-task training and zero-shot generalization to real-world robotics.
By Jiazhao Zhang, Gengze Zhou, Hale Yin and 31 more · arXiv 2606.18112 · PDF · Code
UnverifiedHugging Face's summary; not yet checked by hand.
More from Alibaba (Qwen)
All 78Co-Design Bench: Multimodal Collaborative Coding Evaluation for Agentic ModelsCo-Design Bench evaluates how effectively coding agents update software applications based on user requests with text, images, and other media.
Alibaba (Qwen)23 upvotesRead →Qwen-Image-PE: Learning Visual Design for Text-to-Image Prompt EnhancementQwen-Image-PE improves text-to-image prompts via two-stage training with caption-based reverse distillation and groupwise design rewards, scaling across data, model size, and information density.
Alibaba (Qwen)23 upvotesRead →World-in-Context: Interactive Video World Modeling with In-Context ConditioningWorld-in-Context unifies view, action, and camera controls via explicit tokens and interaction-aware positional encoding to improve interactive video generation and navigation.
Alibaba (Qwen)22 upvotesRead →Beyond Watching and Searching: Benchmarking Agents for Video-centered Deep Research ReportsThis benchmark evaluates how well AI agents can research and synthesize information from videos to produce interactive reports.
Alibaba (Qwen)19 upvotesRead →UserArena: Benchmarking Interactive User Simulation for Long-Horizon Shopping AgentsUserArena introduces a real-world interactive benchmark with long-horizon shopping trajectories and an evaluation framework, alongside On-Policy Trajectory Distillation to improve simulator fidelity for agentic recommendations.
Alibaba (Qwen)55 upvotesRead →SWIFT: Self-guided Weak-to-strong dIFfusion models via Target extrapolationThis training method uses a smaller, frozen model to guide the training of a larger diffusion model, improving quality without increasing inference costs.
Alibaba (Qwen)30 upvotesRead →BlenderWorld: Benchmarking Visual Agents for 3D Creation from Real-World ReferencesThis benchmark evaluates how well AI models can perform complex 3D creation and editing tasks within the Blender software.
Alibaba (Qwen)23 upvotesRead →CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image GenerationThis paper presents an end-to-end framework for generating commercial-grade e-commerce product images by combining metadata extraction, layout planning, and multi-condition generation.
Alibaba (Qwen)25 upvotesRead →
Other foundation models papers
TopicBERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingPre-training a Transformer to fill in masked words, then fine-tuning it, beat task-specific models across language understanding tests.
Google125k citations33 upvotesRead →Language Models are Few-Shot LearnersGPT-3, a 175B-parameter model, does new tasks from a few examples in the prompt, with no fine-tuning.
OpenAI66.4k citations21 upvotesRead →GPT-4 Technical ReportA multimodal model that passes a simulated bar exam with a score around the top 10% of test takers.
OpenAI28.4k citations7 upvotesRead →LLaMA: Open and Efficient Foundation Language ModelsOpen models from 7B to 65B trained only on public data; the 13B model beats GPT-3 on most benchmarks.
Meta22.6k citations25 upvotesRead →The Llama 3 Herd of ModelsOpen models up to 405B parameters that match leading closed models on many tasks.
Meta20.8k citations119 upvotesRead →Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextRecalls details across millions of tokens of text, hours of video and days of audio.
Google DeepMind4,190 citations65 upvotesRead →
About this paper
- Venue
- arXiv.org
- Citations
- 23, 2 influential · Semantic Scholar
- Upvotes
- 29 · Hugging Face
- Lab
- Alibaba (Qwen) · on Companies · on Quarterly
Changes
Citations rose from 22 to 23.
Influential citations rose from 1 to 2.
Citations rose from 13 to 22.
Citations rose from 11 to 13.
Citations rose from 8 to 11.
Influential citations rose from 0 to 1.
Citations rose from 7 to 8.
A first count of 0 influential citations.
A first count of 7 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.