Mechanical Systems Control Lab
About Us
The recent research of MSC lab has focused on intelligent/autonomous mechanical systems and their interaction with humans from manufacturing (industrial robots) to transportation (autonomous driving) with synergies between model-based control methodologies with machine learning.Recent Awards
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Congratulations to Jianyu, Zining, Kiwoo and Daisuke!
The lab celebrated Jianyu Chen, Zining Wang, Kiwoo Shin, and Daisuke Kaneishi’s passing of qualifications exam with a sushi party!
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IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV) 2018 Best Paper Award
Paper “Fast Robot Motion Planning with Collision Avoidance and Temporal Optimization” authored by Hsien-Chung Lin, Changliu liu, and Masayoshi Tomizuka wins the 15th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV) Best Paper Award.
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IEEE International Conference on Intelligent Transportation Systems (ITSC) 2018 Runner-up Best Student Paper Award
Paper “Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control” authored by Zhuo Xu, Chen Tang, and Masayoshi Tomizuka wins the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC) Runner-up Best Student Paper Award.
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IEEE-CASE 2018 Best Application Paper Award
Paper “Grasp Planning for Customizes Grippers by Iterative Surface Fitting” authored by Yongxiang Fan, Hsien-Chung Lin, Te Tang, and Masayoshi Tomizuka wins the IEEE-CASE Best Application Paper Award.
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IEEE Intelligent Vehicles (IV) Symposium 2018 Best Student Paper Award
Paper “Probabilistic Prediction of Vehicle Semantic Intention and Motion” authored by Yeping Hu, Wei Zhan, and Masayoshi Tomizuka wins the 29th IEEE Intelligent Vehicles Symposium (IV) Best Student Paper Award.
Research Update
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Safe OnGO-VIC: Online Gain Optimization for Variable Impedance Control with Control Barrier Functions
We present a safe gain optimization algorithm for variable impedance control with control barrier functions. The algorithm is able to obtain the optimal impedance gain without manully tuning and avoid unwanted collisions in real-time. Check out our webiste page here.
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UrbanLoco: A New Challenge for Urban Mapping and Localization
