The University of Tokyo · Computer Science
Professor Tatsuya Matsushima's research lab specializes in developing data-driven and reinforcement learning-based methods for robotic systems, with a strong emphasis on real-world deployment challenges. The lab focuses on improving deployment efficiency in reinforcement learning by minimizing the number of policy deployments, enhancing safety in meta-learning for visual imitation, and enabling robust adaptation to complex, unstructured environments such as homes. Their work integrates machine learning with robotics to create service robots capable of handling diverse, edge-case scenarios through learned, rather than hand-coded, behaviors.
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Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view
Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans. The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in real home environments, and, importantly, tested for full system performances. For this challenge, we developed an entire household servic
Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view
To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach. In particular, some recent meta-learning methods are shown to solve novel tasks by leveraging their experience of performing other tasks during training. Although studies around meta-learning of robot control have worked on improving the performance, the safety issue has not been fully explored, which is also an importa
Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household servi
Service robot systems, especially household robot systems, have recently achieved adaptability in various environments and tasks by leveraging some machine learning modules. In developing and verifying such data-driven robotic systems, not only the hardware, programs, and communications but also the data and models used are components to be considered. This paper discusses effective data-driven development processes for such service robot systems by introducing and discussing case studies from t
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