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Shih-Min Yang I am a Ph.D. student at Örebro University in Sweden, under the supervision of Martin Magnusson, Todor Stoyanov, and Johannes A. Stork. I am working on the DARKO EU project and affiliated with Wallenberg AI, Autonomous Systems and Software Program (WASP). My research aims to minimize human supervision and enhance learning efficiency in robotic manipulation, enabling robots to adapt to diverse tasks and work toward general-purpose capabilities. In pursuit of this goal,I am focusing on Hierarchical RL, Intrinsic Reward-based Exploration, and Robotic Foundation Models. In 2019, I received my master's degree at the Multimedia Lab in NTNU, Taiwan, focusing on computer vision and domain adaptation under the supervision of Mei-Chen Yeh, and subsequently worked as a Research Associate. Before embarking on my doctoral journey, I worked in the industry as a machine learning engineer. Github / Google Scholar / LinkedIn / Email |
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Research |
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Novelty-based Exploration for Continuous Control in Reinforcement Learning (TBD)
Under Review We propose a novel exploration strategy for reinforcement learning in continuous control tasks with sparse rewards. Keywords: Novelty-based Exploration, Sparse Reward Environment, Reinforcement Learning |
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Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
Shih-Min Yang, Martin Magnusson, Johannes A. Stork, Todor Stoyanov ICRA 2024 IROS 2023 Workshop on Leveraging Models for Contact-Rich Manipulation We suggest using hierarchical reinforcement learning with parameterized primitives to solve occluded grasping tasks without the need for complex manual controller design. Keywords: Contact-rich manipulation, Hierarchical Reinfocement Learning |
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Dynamic Agile Production Robots that Learn and Optimise Knowledge and Operations
DARKO EU Project Örebro University, TUM, Bosch, University of Pisa, EPFL, University of Lincoln, ACT Operations Research DARKO is innovating agile production robots for efficient and safe intralogistics in warehouses. In this project, I mainly contribute to WP2: 3D Perception and Scene Understanding. |
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Object Picking and Constrained Placement by Visual Reasoning
Shih-Min Yang, Yufei Zhu, Rishi Hazra, Kamran Hosseini, Karol Wojtulewicz We designed a robotic system for precise object manipulation, integrating a perception module, visual reasoning module, and an in-hand perception and control module. Yufei, Rishi, and I applied for this project through WASP. In this project, my primary contribution focused on the in-hand perception and control module. |
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From Website Source |