Taerim Yoon

I am a Ph.D. student in Artificial Intelligence at Korea University 🇰🇷, advised by Prof. Sungjoon Choi. Since 2024, I have been actively collaborating with the Computational Robotics Lab at ETH Zurich 🇨🇭, working closely with Prof. Stelian Coros and his team on legged robot control and motion generation.

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Research

My work explores how intelligent robots can perceive, adapt, and act in the real world. I am particularly interested in enabling robots to interpret human intentions, respond in real time, and move with agility through unstructured environments—naturally, like living organisms.

We present a modular high-DOF tendon-driven soft finger and customizable soft hand system capable of diverse dexterous manipulation tasks. By integrating an all-in-one actuation module and enabling flexible finger arrangements, our design supports versatile, task-oriented soft robotic platforms.

Our method enables terrain-aware motion retargeting and time-critical skills such as BackFlip and HopTurn from noisy inputs, including videos or physics-ignorant kinematic frames, and successfully deploys them on real robots.

Our method enables terrain-aware motion retargeting and time-critical skills such as BackFlip and HopTurn from noisy inputs, including videos or physics-ignorant kinematic frames, and successfully deploys them on real robots.

Our goal is to find the right level of autonomy between humans and robots. Fully autonomous control can reduce user controllability, while fully manual control can be burdensome. To address this, we propose a framework that allows the robot to suggest a diverse set of high-quality trajectory options, enabling the user to guide the robot more effectively.

We propose a kinematics-informed neural network (KINN) that enables safe, dexterous, and unified control of hybrid rigid-soft robots by combining rigid body priors with data-driven learning.

This work introduces Zero-shot Active Visual Search (ZAVIS), a system that enables a robot to search for user-specified objects using free-form text and a semantic map of landmarks. By leveraging commonsense co-occurrence and predictive uncertainty, ZAVIS improves search efficiency and outperforms prior methods in both simulated and real-world environments.

Projects

Unpublished, yet interesting projects that I have worked on.

Using reinforcement learning, we train a quadruped robot to perform parkour skills such as jumping over and between obstacles.