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Adam Hung

Email: adamhung@andrew.cmu.edu
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I am a second year Robotics PhD student at Carnegie Mellon University advised by Jeffrey Ichnowski. My interests include learning from human videos, generative modeling for robotics, and manipulation with multi-fingered hands. My research is supported by the NSF Graduate Research Fellowship Program.

Previously, I received a Bachelors in Robotics Engineering from the University of Michigan in 2025. I also worked on reinforcement learning at Symbotic as an intern.

Carnegie Mellon Robotics Institute logo University of Michigan Robotics logo National Science Foundation logo

Publications (*indicates equal contribution)

3PoinTr: 3D Point Tracks for Learning Manipulation from Unconstrained Human Videos
Adam Hung, Bardienus P. Duisterhof, Jeffrey Ichnowski
arXiv, 2026
[Website] [Paper] [Video]
3PoinTr learns manipulation from unconstrained human videos: videos where the human demonstrator can act freely rather than mimicking target robot kinematics. 3PoinTr first predicts dense 3D point tracks, and then conditions a closed-loop multitask policy on these tracks.
RoPotter: Toward Robotic Pottery and Deformable Object Manipulation with Structural Priors
Uksang Yoo*, Adam Hung*, Jonathan Francis, Jean Oh, Jeffrey Ichnowski
IEEE International Conference on Humanoid Robots (Humanoids), 2024
Best Oral Presentation Finalist Award
[Website] [Paper] [Video]
RoPotter learns to create bowls on a pottery wheel by using structural priors to inform a continuously deforming mesh representation of the clay.
AVO: Amortized Value Optimization for Contact Mode Switching in Multi-Finger Manipulation
Adam Hung, Fan Yang*, Abhinav Kumar*, Sergio Aguilera Marinovic, Soshi Iba, Rana Soltani Zarrin, Dmitry Berenson
arXiv, 2025
[Paper] [Video]
Long-horizon trajectory optimization can be made tractable by decomposing the problem into contact-mode-specific subtasks. AVO bridges these subtasks with a learned value-function ensemble that guides each subtask toward states that are advantageous for future subtasks, and simultaneously reduces online computation.
MapExRL: Learning Efficient Indoor Mapping and Exploration using Predicted Global Map Context
Narek Harutyunyan, Brady Moon, Seungchan Kim, Adam Hung, Cherie Ho, Sebastian Scherer
International Conference on Advanced Robotics (ICAR), 2025
[Website] [Paper] [Video]
MapExRL uses global indoor map predictions and a budget-aware RL policy to exploit structure in indoor environments, outperforming greedy algorithms and other prior methods on long-horizon indoor exploration.
SKOOTR: A SKating, Omni-Oriented, Tripedal Robot
Adam Hung, Challen Enninful Adu, Talia Moore
IEEE International Conference on Robotics and Automation (ICRA), 2025
[Website] [Paper] [Video]
SKOOTR is a brittlestar-inspired tripedal robot that explores the advantages of radially symmetric form-factors. SKOOTR is highly stable and maneuverable, and enables several novel forms of locomotion.