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🚀 🚀 Many colleagues mentioned the article (36 pages) explaining Neural-Symbolic AI is too lengthy to grasp quickly. So, we made a brief blog (sairlab.org/iseries/) to summarize it. You can learn it in 10 minutes!

🚀🚀We are introducing the Best Paper Award (a certificate and a $500 prize) in Computer and Robot Vision for papers published in the IEEE Transactions on Robotics (T-RO) from Jan 1, 2023 to Dec 31, 2023. Please nominate a paper by filling out the form: forms.gle/kUdRW3JtCbsh9K…

The IJRR Special Issue on Foundation Models and Neural-Symbolic AI for Robotics is now on a rolling basis. New manuscripts can be submitted until Nov 2024. All submissions will receive their first decision in about 3 months from the day of submission. See sairlab.org/ijrr/

🚀 We're excited to open-source AirSLAM (an upgrade from AirVO)! With dual modes (V-SLAM/VI-SLAM), it runs at 73Hz on PC and 40Hz on Jetson and delivers extremely high accuracy and robustness against illumination challenges. Check out the code at github.com/sair-lab/AirSL…

🚀Excited to share our latest work at #ECCV2024! Presenting iMatching: Imperative Correspondence Learning, a groundbreaking self-supervised framework for feature correspondence with bundle adjustment 🎯. Achieving up to 30% accuracy boost on SOTA models! arxiv.org/pdf/2312.02141

🚀 Excited to present PhysORD at #IROS2024! It is a neuro-symbolic model for motion prediction in off-road driving, enforcing the physical law of conservation into ML. PhysORD outperforms in accuracy, efficiency, and long-term generalization! arxiv.org/abs/2404.01596

🚀 Exciting news! Check out our latest work on the Multiple Traveling Salesman Problem (iMTSP) in IROS'24! We're pushing the boundaries of efficient and scalable AI-driven scheduling solutions. Explore its source codes and paper at sairlab.org/iMTSP/ Chen Wang

🎉 Success! We've applied Imperative Learning (IL) to Humanoid Robots for footstep planning! Next time let's walk a humanoid robot under diverse terrain! Check out the latest preprint at sairlab.org/iwalker/ Taimeng Fu Chen Wang #Robotics #AI #HumanoidRobots

Uncertainty modeling, grounded in 3D metric space, unlocks general-purpose state estimation. mac-vo.github.io MAC-VO outperforms SOTA methods and achieves reliable accuracy across platforms and datasets with no multi-frame optimization, loop closure, or fine-tuning! 👇🧵

🎉🎉🎉 I’m honored to contribute as one of the chapter authors with an incredible team for this incredible SLAM Handbook! This is just the beginning, and your feedback will help make it even better! 🙌

I am honored to be part of SAIR Lab!

Congrats to Kuan Xu! You may try the code at github.com/sair-lab/AirSL…

By integrating hundreds of users’ feedback and adding data for 2025, we are now able to provide a more comprehensive Roboranking, the robotics faculty & the university ranking system. Check here to see the latest version of roboranking.org

We are happy to announce that the DDL for the ICRA'25 workshop call for papers on "Foundation Models and Neuro-symbolic AI for Robotics” have been extended to April 1 (no more extension). More information about it can be found at sairlab.org/icra25/

Excited to introduce our latest research "VL-Nav: A Real-Time Vision-Language Navigation System with Spatial Reasoning". It enables tasks like "find a person wearing black" and runs 30 Hz on a Jetson Orin NX! Check more details at sairlab.org/vlnav/

Great to see Neuro-symbolic AI gaining momentum in computer vision, especially in the context of robotics and embodied AI problems. Inspiring talk by Chen Wang at the #CVPR2025 AC Workshop! #CVPR2025 #EmbodiedAI #NeuroSymbolic #ComputerVision #AIResearch

This is the first attempt of imperative learning in humanoid robotics! Very Excited! You may check more imperative learning series articles at sairlab.org/iseries/