Welcome to the Intelligent Systems and Learning (ISL) Lab at DGIST, led by Prof. Daehee Park. Our mission is to bridge the gap between artificial intelligence and the physical world.
Moving beyond traditional model scaling paradigms, we strive to advance a new generation of practical and deployable Physical AI capable of operating reliably in complex, real-world environments.

We are always looking for passionate students who want to push the boundaries of AI. Join us →

Research Topics

Self-Evolving A.I.

Rollout

Self-Evolving A.I.

Evolving

Self-Evolving A.I.

Building AI systems that autonomously improve through interaction with the environment, enabling continual learning, test-time adaptation, and self-refinement without human intervention.

Robotics AI

AgileX

Robotics AI

Arx-X5

Robotics AI

Advancing the frontier of Embodied AI by integrating multimodal perception with real-world robotic control to automate complex physical tasks.

Knowledge Integration

Knowledge Integration

Research on integrating knowledge from multiple sources and modalities to enhance AI systems' understanding and decision-making capabilities.

Autonomous Driving

Chat Driving

Autonomous Driving

Attack Driving

Autonomous Driving

Enabling safe and intelligent autonomous driving through trajectory prediction, motion planning, and multimodal scene understanding for real-world environments.

World Model

World Model Sample 1

World Model

World Model Sample 2

World Model

Developing internal models that allow AI systems to predict, simulate, and reason about future states of the world, enabling more robust planning and decision-making under uncertainty.

Human Motion Modeling

3D Hand Pose Sample

Human Motion Modeling

2D Hand Pose Sample

Human Motion Modeling

Research on modeling and generating human motion using generative AI techniques.

On-Device AI

On-Device AI

Enabling reliable AI inference on resource-constrained devices by developing efficient model compression, adaptation, and deployment techniques for foundation models in real-world physical systems.

Computer Vision & Multimodal A.I.

Computer Vision & Multimodal A.I.

Developing high-level scene understanding models such as semantic segmentation, object detection, and scene reconstruction for autonomous systems, robotics, and other applications.

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