Marco Pavone

Kudos to @ShinpeiKato and #Autoware for the lightning-fast integration of @nvidia Alpamayo 2 Edge into Autoware! Exciting to see it already experiencing the streets of Tokyo!

A major milestone for the @nvidia #Alpamayo Open Platform: Introducing Alpamayo 2 Edge!

Alpamayo 2 Edge is the first real-time, #Thor-ready reasoning model in the Alpamayo family, bringing adaptive AI reasoning to the vehicle in real time.

Alpamayo 2 Edge delivers state-of-the-art performance across a broad range of benchmarks, and feedback from our early-access partners has been overwhelmingly positive.

On the technical side, Alpamayo 2 Edge brings several important innovations, from adaptive reasoning to being the first Alpamayo model post-trained in closed loop through reinforcement learning in AlpaGym, as well as human-calibrated reasoning evaluation through AlpaJudge. The model also comes with recipes for quantization and inference acceleration to squeeze maximum performance out of onboard compute.

Learn more about Alpamayo 2 Edge: developer.nvidia.com/blog/running-a…

Try it out on Hugging Face: huggingface.co/nvidia/Alpamay…

Excited to see what the community will build with it! And stay tuned for more on how we're extending these capabilities to off-road autonomy and robotics!

#Alpamayo #PhysicalAI #AutonomousDriving #Robotics

@nvidia @NVIDIAAI @NVIDIADRIVE @NVIDIARobotics

@nvidia Alpamayo has pioneered #reasoning-based autonomy. Now, we’re expanding the capabilities of the Alpamayo open platform to enable long-horizon autonomy for any robot in the real world—from vehicles and mobile manipulators to humanoids.

At #CoRL2026, we’re excited to showcase a proof point of what the platform enables: agentic, reasoning-based whole-body intelligence for humanoids.

Stop by our demo to see it in action and explore what we can build together with the Alpamayo open platform. And stay tuned for an exciting announcement later this week!

🗓️ Tuesday, November 10 | 10:00 AM–6:30 PM CT
📍 Booth 4, Lone Star Ballroom, JW Marriott Austin

CoRL demo program: corl.org/program/demos

#Alpamayo #PhysicalAI #Robotics #CoRL2026

@NVIDIAAI @NVIDIARobotics @NVIDIADRIVE

Can an AI agent learn a pool table’s physics by playing on it? 🎱

Closing the visual gap between reality and simulation is one thing. Closing the dynamics gap is much harder.

In this fun project, we let an agent take real shots, observe the outcomes, and iteratively improve its simulation of the physical world.

The idea: rather than just observing the world, let agents actively experiment with it and learn its physics through interaction.

A fun demonstration of a key capability for Physical AI: learning the physics of the real world through autonomous experimentation.

A @corl_conf late breaking contribution. If you plan to attend #CoRL, see you there!

In collaboration with @XilunZhangXZ @MatthewFoutter
@Luis_A_Pabon @Liam862373

Check it out: cue-dynamics.github.io

#PhysicalAI #Robotics #AgenticAI #RobotLearning

@StanfordAILab @StanfordASL

How can #Physical #AI systems maintain memory, reason over past observations, and use that information to make better decisions?

Memory is an emerging research direction for Physical AI, where agents must operate under partial observability and make decisions informed by the history of their interactions with the world.

In our new paper, Vision-Language-Action Autonomous Driving Agent with Language-based Memory, we introduce AD-Memo, a driving agent that uses language-based memory to capture decision-relevant information about surrounding objects and leverage it in subsequent reasoning and driving decisions.

This builds on our earlier work, Planning-aligned Token Compression for Long-Context Autonomous Driving, where we explored how to selectively preserve historical information by aligning memory compression with driving intent and planning objectives.

Together, these works explore an important direction for Physical AI: building agents that learn what information to retain, how to represent it, and how to use it to make better decisions over time.

📄 Language-based memory paper: arxiv.org/abs/2609.38641 (project webpage: kaiyan289.github.io/projects/ad-me…)

📄 Planning-aligned token compression paper: arxiv.org/abs/2606.07464

Kudos to all the authors, especially the lead authors, Kai Yan and Zhixuan Liang, and their mentors, @WenjieLuo and @Yuxiao_Chen_.

#PhysicalAI #AutonomousDriving #Robotics #VLA

@NVIDIADRIVE @NVIDIAAI