biography

Azumi Maekawa is a Ph.D. student at the University of Tokyo, in Information Somatics Lab. His research interests are in the areas of Robotics, Machine Learning, Design, Digital Fabrication, and Human Robot Interaction. He has presented his works in domestic and international exhibitions and conferences. The focus of his current work is the motion with the physical bodies.

2018
M.A.S. in Engineering (Mechanical Engineering), The University of Tokyo

2016
B.A. in Engineering (Mechanical Engineering), The University of Tokyo

works

tug

The Tight Game

Physical assistance can alleviate individual differences of abilities between players to create well-balanced inter-personal physical games. However, ‘explicit’ intervention can ruin the players’ sense of agency, and cause a loss of engagements in both the player and audience. We propose an implicit physical intervention system ”The Tight Game” for ‘Tug of War’ a one-dimensional physical game. Our system includes four force sensors connected to the rope and two hidden high torque motors, which provide realtime physical assistance. We designed the implicit physical assistance by leveraging human recognition of the external forces during physical actions. In The Tight Game, a pair of players engage in a tug of war, and believe that they are participating in a well balanced, tight game. In reality, however, an external system or person mediates the game, performing physical interventions without the players noticing.

publication:

  • Azumi Maekawa, Shunichi Kasahara, Hiroto Saito, Daisuke Uriu, Ganesh Gowrishankar, and Masahiko Inami. "The Tight Game: Implicit Force Intervention in Inter-personal Physical Interactions on Playing Tug of War." ACM SIGGRAPH 2019 Emerging Technologies, ACM, 2020.

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PickHits

PickHits

Experiences of hitting targets cause a great feeling. We propose a system for generating this experience computationally. This system consists of external tracking cameras and a handheld device for holding and releasing a thrown object. As a proof-of-concept system, we developed the system based on two key elements: low-latency release device and constant model-based prediction. During the user’s throwing motion, the ballistic trajectory of the thrown object is predicted in real time, and when the trajectory coincides with the desired one, the object is released. We found that we can generate a computational hitting experience within a limited range space.

publication:

  • Azumi Maekawa, Seito Matsubara, Sohei Wakisaka, Daisuke Uriu, Atsushi Hiyama, and Masahiko Inami. "Dynamic Motor Skill Synthesis with Human-Machine Mutual Actuation." In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-12.

  • Azumi Maekawa, Seito Matsubara, Atsushi Hiyama, and Masahiko Inami. "PickHits: Hitting Experience Generation with Throwing Motion via a Handheld Mechanical Device." ACM SIGGRAPH 2019 Emerging Technologies. ACM, 2019.

credit:

Azumi Maekawa, Seito Matsubara

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Stand

Stand

The hardware design of the robots usually reflects its purpose. For example, leg for walking, arm for reaching, hand for grasping, etc. The function of mechanical systems are explicitly defined by designers and the simple shape consisting of straight lines or circular arcs is reasonable for its clarified function. On the other hand, our real environment is full of diverse shapes. The complex shape has been formed by various factors, biological growth, aging, weathering and so on. Utilizing diverse objects to create robots, we might be able to see behaviors and motions we’ve never seen. We aim to find unpredictable motions we could not discover with simple shapes. In this work, as a primitive function of the robot, we focus on stand-up behavior. For the materials that bricolage the robot, we select tree branches as objects with diverse shapes. The robots' poses are generated aiming to maximize the height of the bodies. Robots with diverse body shapes change their pose by repeating trial and error in real time. Through the process of learning, this work portrays new functions and meanings given to commonplace objects.

exhibition:

credit:

Azumi Maekawa

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Naviarm

Naviarm

We present a wearable haptic assistance robotic system for augmented motor learning called Naviarm. This system comprises two robotic arms that are mounted on a user’s body and are used to transfer one person’s motion to another offline. Naviarm pre-records the arm motion trajectories of an expert via the mounted robotic arms and then plays back these recorded trajectories to share the expert’s body motion with a beginner. The Naviarm system is an ungrounded system and provides mobility for the user to conduct a variety of motions. We focus on the temporal aspect of motor skill and use a mime performance as a case study learning task. We verified the system effectiveness for motor learning using the conducted experiments. The results suggest that the proposed system has benefits for learning sequential skill.

publication:

  • Azumi Maekawa, Shota Takahashi, MHD Yamen Saraiji, Sohei Wakisaka, Hiroyasu Iwata, and Masahiko Inami. "Naviarm: Augmenting the Learning of Motor Skills using a Backpack-type Robotic Arm System." In Proceedings of the 10th Augmented Human International Conference 2019, p. 38. ACM, 2019
  • Azumi Maekawa, Shota Takahashi, MHD Yamen Saraiji, Sohei Wakisaka, Hiroyasu Iwata, and Masahiko Inami. "Demonstrating Naviarm: Augmenting the Learning of Motor Skills using a Backpack-type Robotic Arm System." In Proceedings of the 10th Augmented Human International Conference 2019, p. 48. ACM, 2019.

credit:

Azumi Maekawa, Shota Takahashi, MHD Yamen Saraiji, Sohei Wakisaka, Hiroyasu Iwata, and Masahiko Inami

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Walk

Photo by Yasushi Kato

Walk

This project aims at creating bricolages of robots out of tree branches found at hand. Through the process in which natural objects learn how to walk by themselves, the artwork portrays the perspectives of objects. Unlike the top-down process where functions of mechanical systems are explicitly defined by designers, this project puts an emphasis on the emergence of functions, which is a bottom-up process where found objects seek for the function as a whole.

publication:

  • Azumi Maekawa, Ayaka Kume, Hironori Yoshida, Jun Hatori, Jason Naradowsky, and Shunta Saito. "Improvised Robotic Design with Found Objects." Workshop at NeurIPS 2018

exhibition:

credit:

Azumi Maekawa, Jun Hatori, Shunta Saito, Hironori Yoshida, Ayaka Kume

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Arial-Biped

Photo by Yasushi Kato

Arial-Biped

There has been a desire to create robots that are capable of life-like motions and behaviors since ancient times. As a life-like motion, we focus on the bipedal walking, which is one of the key features of a legged robot. Movement of the legged robot is highly restricted by gravity. When its body shape is defined, the possible walking gait is almost determined accordingly. Therefore, it is difficult to realize arbitrary body design and dynamic walking motion with light steps like living things in a legged robot. Aerial-Biped is a prototype for exploring a new experience with a physical biped robot. In this work, using a quadrotor, we aim to separate the body shape design and motion design. By releasing the biped robot from gravity, it can relax the limitation of the robot's physical motion. The motion of Aerial-Biped is created in real time according to the movement of the quadrotor by the motion generator learned using deep reinforcement learning. We can observe various gaits emerge based on the successively changing quadrotor's movements.

publication:

  • Azumi Maekawa, Ryuma Niiyama, and Shunji Yamanaka. "Pseudo-Locomotion Design with a Quadrotor-Assisted Biped Robot." In 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2462-2466. IEEE, 2018.
  • Azumi Maekawa, Ryuma Niiyama, and Shunji Yamanaka. "Aerial-biped: a new physical expression by the biped robot using a quadrotor." ACM SIGGRAPH 2018 Emerging Technologies. ACM, 2018.

exhibition:

media:

credit:

Azumi Maekawa, Shunji Yamanaka

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see more works >

publication

paper (peer-reviewed)

  • Azumi Maekawa, Kei kawamura, and Masahiko Inami. "Dynamic Assistance for Human Balancing with Inertia of a Wearable Robotic Appendage." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). To appear

  • Azumi Maekawa, Seito Matsubara, Sohei Wakisaka, Daisuke Uriu, Atsushi Hiyama, and Masahiko Inami. "Dynamic Motor Skill Synthesis with Human-Machine Mutual Actuation." In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-12. Honorable Mention Award [Top 5%]

    [pdf] [doi]
  • Azumi Maekawa, Shota Takahashi, MHD Yamen Saraiji, Sohei Wakisaka, Hiroyasu Iwata,and Masahiko Inami. "Naviarm: Augmenting the Learning of Motor Skills using a Backpack-type Robotic Arm System." In Proceedings of the 10th Augmented Human International Conference 2019, pp. 1-8.

    [pdf] [doi]
  • Azumi Maekawa, Ryuma Niiyama, and Shunji Yamanaka. "Pseudo-Locomotion Design with a Quadrotor-Assisted Biped Robot." In 2018 IEEE International Conference on Robotics and Biomimetics(ROBIO), pp. 2462-2466.

    [pdf] [doi]

demo & workshop (peer-reviewed)

  • Azumi Maekawa, Shunichi Kasahara, Hiroto Saito, Daisuke Uriu, Ganesh Gowrishankar, and Masahiko Inami. "The Tight Game: Implicit Force Intervention in Inter-personal Physical Interactions on Playing Tug of War" ACM SIGGRAPH 2020 Emerging Technologies. To appear

  • Azumi Maekawa, Seito Matsubara, Atsushi Hiyama, and Masahiko Inami. "PickHits: Hitting Experience Generation with Throwing Motion via a Handheld Mechanical Device." ACM SIGGRAPH 2019 Emerging Technologies.

    [pdf] [doi]
  • Azumi Maekawa, Shota Takahashi, MHD Yamen Saraiji, Sohei Wakisaka, Hiroyasu Iwata, and Masahiko Inami. "Demonstrating Naviarm: Augmenting the Learning of Motor Skills using a Backpack-type Robotic Arm System." In Proceedings of the 10th Augmented Human International Conference 2019, p. 48. Best Demo Award

    [pdf] [doi]
  • Azumi Maekawa, Ayaka Kume, Hironori Yoshida, Jun Hatori, Jason Naradowsky, and Shunta Saito. "Improvised Robotic Design with Found Objects." Workshop at Thirty-second Conference on Neural Information Processing Systems (NeurIPS), 2018 (Oral Presentation).

    [pdf]
  • Azumi Maekawa, Ryuma Niiyama, and Shunji Yamanaka. "Aerial-biped: a new physical expression by the biped robot using a quadrotor." ACM SIGGRAPH 2018 Emerging Technologies.

    [pdf] [doi]

exhibition

awards

media

contact

  • azumimaekawa[at]gmail.com