We’re now accepting applications for the 2026–2027 NVIDIA Graduate Fellowships! If you’re passionate about advancing cutting-edge reasoning models for Physical AI applications 🚗🤖, apply here: research.nvidia.com/graduate-fello… — and be sure to select “Autonomous Vehicles.”
@NVIDIAAI
Can we use simulation to validate Physical AI? Yes—with far fewer real-world tests. We propose a control variates–based estimation framework that pairs sim & real data to dramatically cut validation costs. #AI#Robotics#Sim2Real"
Paper: arxiv.org/pdf/2506.20553@NVIDIADRIVE
Happy to share our latest work on efficient sensor tokenization for end-to-end driving architectures! arxiv.org/abs/2506.12251
We introduce a novel way to tokenize multi-camera input for AV Transformers that is resolution- and camera-count-agnostic, yet geometry-aware
🧵👇
Don’t miss this deep dive into the future of autonomous vehicles!
Excited to present about how foundation models are transforming AV technology with @ALVAREZ_JOSEM at #GTC25!
Check out all the session details below 👇
Don’t miss this deep dive into the future of autonomous vehicles!
Excited to present about how foundation models are transforming AV technology with @ALVAREZ_JOSEM at #GTC25!
Check out all the session details below 👇
For the first time ever, @nvidia is hosting an AV Safety Day at GTC - a multi-session workshop on AV safety.
We will share our latest work on safe AV platforms, run-time monitoring, safety data flywheels, and more! #AutonomousVehicles#AI at #GTC25
➡️ nvda.ws/3Xc3xPo
Want a haptic force feedback glove?
Meet DOGlove! 🖐✨ A precise, low-cost (~$600), open-source glove for dexterous manipulation. Teleoperate a dexterous hand to squeeze condensed milk on bread 🥪 or collect high-quality data for imitation learning.
Check it out! 🎥👇…
AI4I, the Italian Institute of Artificial Intelligence for Industry (ai4i.it), has launched an international call for Heads of R&D Units (ai4i.it/call-for-head-…).
This is a unique opportunity to shape the AI roadmap in Italy and beyond! @FabioPammolli
Complementing DreamDrive, I am thrilled to introduce STORM, which enables fast scene reconstruction with a single feed-forward model.
STORM transforms camera logs into dynamic 3D models - in real time!
Web: jiawei-yang.github.io/STORM/
Paper: arxiv.org/abs/2501.00602
Introducing DreamDrive, which combines the complementary strengths of generative AI (video diffusion) and neural reconstruction (Gaussian splatting) to transform any street-view image into a dynamic 4D driving scene!
Web: pointscoder.github.io/DreamDrive/
Paper: arxiv.org/abs/2501.00601
🔍 Inference-time scaling is a key focus in foundation models, but its origins trace back to model-based RL (MBRL). In MBRL, a critical challenge arises from reconciling the conflict between "world model prediction" and "task reward"—the gap between next-state prediction accuracy…
[1/4] 🌟Sneak Peek: SPARK in Action! 🦾
Previewing Safe Protective & Assistive Robot Kit (SPARK)—a modular toolbox designed to enhance safety in humanoid autonomy and teleoperation.
Safety isn't just a feature—it's the foundation for humanoids to truly integrate into human life.…
How can we best use LLMs in an autonomy stack? An exciting prospect is to exploit their generalist experience to reason about anomalies. And one can do this in real time by leveraging their embeddings in a fast&slow decision making architecture. Work led by @RohanSinhaSU#RSS2024
How can we best use LLMs in an autonomy stack? An exciting prospect is to exploit their generalist experience to reason about anomalies. And one can do this in real time by leveraging their embeddings in a fast&slow decision making architecture. Work led by @RohanSinhaSU#RSS2024
Can we use NeRFs in the wild? Introducing DistillNeRF, a framework for *generalizable* 3D scene representation prediction from sparse multiview image inputs, using distillation from per-scene optimized NeRFs and visual foundation models. arxiv.org/pdf/2406.12095@NVIDIAAI
Can we use NeRFs in the wild? Introducing DistillNeRF, a framework for *generalizable* 3D scene representation prediction from sparse multiview image inputs, using distillation from per-scene optimized NeRFs and visual foundation models. arxiv.org/pdf/2406.12095@NVIDIAAI
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