We developed a real-time 3D semantic mapping neural network layer to combine the advantages of our Bayesian inference semantic mapping with deep learning. We can now learn hyperparameters and kernels, run in real-time, and give uncertainty estimates for occupancy/semantics.

Context-aware navigation requires object/terrain-level understanding - in support of this, we are thrilled to release CODa, the largest multi-class, multi-modal urban robotics dataset to date. amrl.cs.utexas.edu/coda/ Work done with a fantastic team from UT Austin's Autonomous Mobile Robotics Laboratory! A short 🧵, 1/7

What’s exciting about #GAIA1 is that it's not just a simulation, but a full world model that understands how the world works Here's two different futures where a reversing car (1) pulls off the street or (2) pulls out suddenly requiring us to brake More of my fav examples in 🧵

I like my reviewer 2 😊 My first submission to CVPR got accepted! #CVPR2024 — Final Rating: 4: Weak Accept Final Rating Justification: The authors provide satisfactory responses to my concerns, and as a result, I will modestly increase the rating.

Animal-like robots are the future. Introducing Verdie, a physical agent for sustainable outdoor maintenance capable of learning multiple power tools. Verdie was trained in the NVIDIA Isaac simulator for just 5 hours before string trimming lawns for the first time. Verdie

We present🌊AquaBot🤖: a fully autonomous underwater manipulation system powered by visuomotor policies that can continue to improve through self-learning to perform tasks including object grasping, garbage sorting, and rescue retrieval. aquabot.cs.columbia.edu more details👇

My name is on the next mission to Mars on NASA InSight. Send your name today! mars.nasa.gov/syn/insight/?c…

I believe ur the one in my life. instagram.com/p/BcW4d1yBalR/

The big winner is Uzi as the LPL All-Stars win #AllStar2017 By Kien Lam Read now at: lolesports.com/en_US/articles…