I am a dedicated robotic learning researcher and engineer with a strong passion for advancing the field of robotics through innovative research and practical engineering solutions. My work focuses on developing cutting-edge machine learning algorithms and integrating them into robotic systems to enhance their capabilities and efficiency. I am enthusiastic about collaborative opportunities with academia and industry partners globally, aiming to drive impactful advancements in robotic technology.
@article{oh2024ispil,title={{ISPIL}: Interactive Sub-Goal-Planning Imitation Learning for Long-Horizon Tasks With Diverse Goals},author={Ochoa, Cynthia and Oh, Hanbit and Kwon, Yuhwan and Domae, Yukiyasu and Matsubara, Takamitsu},journal={IEEE Access},year={2024},volume={12},number={},pages={197616-197631},keywords={Robots;Switches;Imitation learning;Training;Symbols;Planning;Cause effect analysis;Atoms;Artificial intelligence;Accuracy;Interactive imitation learning;learning-to-plan;hierarchical policy},doi={10.1109/ACCESS.2024.3521302},url={https://ieeexplore.ieee.org/abstract/document/10811934},equal={test},}
Leveraging Demonstrator-Perceived Precision for Safe Interactive Imitation Learning of Clearance-Limited Tasks
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actual human behavior (e.g., multiple optimal actions), thereby limiting robot generalizability, applicability, and demonstration feasibility. To address this, this paper proposes designing imitation learning algorithms with a focus on utilizing human behavioral characteristics, thereby embodying principles for capturing and exploiting actual demonstrator behavioral characteristics. This paper presents the first imitation learning framework, Bayesian Disturbance Injection (BDI), that typifies human behavioral characteristics by incorporating model flexibility, robustification, and risk sensitivity. Bayesian inference is used to learn flexible non-parametric multi-action policies, while simultaneously robustifying policies by injecting risk-sensitive disturbances to induce human recovery action and ensuring demonstration feasibility. Our method is evaluated through risk-sensitive simulations and real-robot experiments (e.g., table-sweep task, shaft-reach task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate the improved characterisation of behavior. Results show significant improvement in task performance, through improved flexibility, robustness as well as demonstration feasibility.
@article{oh2022bdi,title={{Bayesian Disturbance Injection}: Robust Imitation Learning of Flexible Policies for Robot Manipulation},author={Oh, Hanbit and Sasaki, Hikaru and Michael, Brendan and Matsubara, Takamitsu},journal={Neural Networks},volume={158},pages={42-58},year={2023},issn={0893-6080},doi={https://doi.org/10.1016/j.neunet.2022.11.008},url={https://www.sciencedirect.com/science/article/pii/S089360802200449X},keywords={Imitation learning, Disturbance injection, Human behavior characteristics, Robotic manipulation},}
