Toward Next-Gen Physical AI: Research Group at Applied Intuition

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ADAS (先進運転支援システム) と自動運転 (AD)は、データ、機械学習 (ML)、シミュレーションに対する新たなアプローチを必要とするエンド ツー エンド (E2E) アーキテクチャをますます活用しています。車内体験は、インフォテインメント、モバイルアプリケーション、ボディコントロールなど、ますますソフトウェア定義型になってきています。自動車メーカーは、UN R157 や UN R171などの規制に準拠しつつ、開発を加速させながら次世代の機能開発を進める必要があります。

This group, led by Dr. Wei Zhan, includes leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Join our group to work with a team of top researchers who each have thousands of citations across their work and many top-tier publications, including a CVPR Best Paper lead author, to advance next-generation physical AI.

製品を見る

Massive data from large fleets processed by automated data engine

AI搭載のシミュレーターで、ドライブログをインタラクティブな世界に変換し、クローズドループADASテストとトレーニングを実現し

Various fleets to test and deploy our autonomy — including cars, trucks, and offroad mining

Large fleets across various products globally to test and deploy autonomy algorithms developed by our researchers and engineers, from autonomous cars tested in challenging urban scenarios to self-driving trucks and mining/construction vehicles for both onroad and offroad scenes.

Robot fleet and human data collection

Robot fleets with humanoids, mobile manipulators, and table-top dual-arm robots with cutting-edge, tactile-aware dexterous hands, as well as various human data collection devices such as MoCap, headset, and gloves.

Large-scale ML infrastructure and neural simulation with efficient tools

As a world-leading tool provider for autonomous systems, our researchers are supported by various efficient tools, high-quality neural simulation and synthetic data at scale in closed loop, and ML infrastructure toward large-scale training with thousands or more of GPUs.

Research Focuses

We address the following research fields in our group to advance next-generation physical AI:

World-action foundation model pre-training

The next-generation foundation model for physical AI should be pretrained with better balanced and grounded multi-modal data from ego action to world (vision, behavior, physics) and language toward the concrete tasks. We accordingly address the following topics:
Feed-forward/generative 4D reconstruction and world foundation model for reactive generation of 4D world conditioned on ego action with high throughput
Pretraining of world-action model with grounded modalities including vision, physics, and language

Reinforcement learning and foundation model post-training

Post-training of foundation models such as world-action and vision-language-action models is highly crucial for performance improvement, safety assurance and preference alignment for physical AI applications. In view of this, we address the following research fields:
Closed-loop reinforcement-learning-based post-training of foundation models supported by high-fidelity, scalable, high-throughput simulation constructed/learned from large-scale real-world data
Self-play reinforcement learning supported by high-throughput simulation in combination with human data imitation toward robust and human-like physical AI even with low data.

Robot learning and data

Toward capable robotic generalists, physics-aware modalities become demanding and the large-scale data to enable the generalists is not as straightforward to obtain as driving applications. Therefore, we have special focus on various paradigms and methods of robot learning and data to properly scale up data from robot, human, and synthetic world with physics-aware modalities, and corresponding design methods for utilization of the data to achieve robotic generalists.

Publications and Projects

S2GO: Streaming Sparse Gaussian Occupancy Prediction

Jinhyung Park, Chensheng Peng, Yihan Hu, Wenzhao Zheng, Kris Kitani, Wei Zhan
ICLR 2026
arXiv Preprint
Website

SPACeR: Self-Play Anchoring with Centralized Reference Models

Wei-Jer Chang, Akshay Rangesh, Kevin Joseph, Matthew Strong, Masayoshi Tomizuka, Yihan Hu, Wei Zhan
ICLR 2026
arXiv Preprint
Website

Open Positions

Self-Driving

Research Scientist - 3D Vision and Generation, Self-Driving
Research Scientist - Reinforcement Learning, Self-Driving
Research Engineer - 3D Vision and Generation, Self-Driving
Research Engineer - Reinforcement Learning, Self-Driving
Research Intern - 3D Vision and Generation, Self-Driving
Research Intern - Reinforcement Learning, Self-Driving

Robotics

Research Scientist - World-Action Foundation Model, Robotics
Research Scientist - Reinforcement Learning, Robotics
Research Engineer - Robot Learning
Research Engineer - Robotic Hardware, Simulation and Data
Research Intern - World-Action Foundation Model, Robotics
Research Intern - Reinforcement Learning, Robotics
Research Intern - Robotic Hardware, Simulation and Data

ML infrastructure

Research Engineer - AI/RL Infrastructure
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