Member Login

News

ARTICLE: Collaborative Robotics in Japan 

POSTED: 17 Jul, 2026

 

By Dr Sheila Sutjipto

Last year, Professor Teresa Vidal-Calleja and I travelled to Japan primarily for a focused research collaboration at the Tokyo University of Science (TUS). While our joint work at TUS constituted the core of the trip, Teresa also took the opportunity to visit and deliver talks at several other leading research institutions across the country. This opportunity provided a comprehensive look at the broader Japanese robotics landscape, spanning space exploration, disaster recovery, social robotics, and human-robot interaction. 

A Landscape of Service and Innovation in Robotics Research 

A recurring theme across the institutions Teresa visited was the development of service and assistive robots designed to support human society. At Tohoku University, the Smart Robotics Design Lab is developing human-assist robots for caregiving and healthcare to support an aging population. Similarly, the Nara Institute of Science and Technology (NAIST) focuses on integrating machine learning and artificial to advance human-robot collaboration in real world applications. 

Figure: A robotic system designed for service robotics by the Smart Robot Design Lab in Tohoku University. It is trained to manipulate a deformable object like a t-shirt addressing housework tasks.
Figure: A humanoid learning to navigate environments with obstacles at the Neuro-Robotics lab in Tohoku University 
Figure: Companion robots used as a research platform for Human-Robot Interaction by the Smart Robot Design Lab at Tohoku University 
Figure: A robotic system designed by researchers from the Robot Learning lab at NAIST. It is designed to perform automated grinding of workpieces. 

 

The applications she encountered were highly diverse. Her visits included the Institute of Science – Tokyo’s Robotics and AI Laboratory, which focuses on robot audition and environment understanding, as well as Hokkaido University, where they address research in automated infrastructure inspection and autonomous driving for snowy environments.

Additionally at Tohoku University, she observed autonomous systems and sensing units designed for extreme conditions at the Tough Robotics Lab and extraterrestrial exploration at the Space Robotics Lab. Teresa also attended The International Conference on Space Robotics (ISpaRo 2025) whilst in in Sendai. This event brought together aerospace experts, academia, and industry professionals to discuss innovations in space exploration. 

Figure: Sensing units designed for dogs working in search and rescue 
Figure: A test environment at the Space Robotics Laboratory at Tohoku University
Figure: Full scale model of the JAXA Kibo ISS Module showcased at ISpaRo 

 

These visits and talks provided valuable insights into the scope and societal focus of Japan’s robotics ecosystem, highlighting the diverse platforms currently being developed by researchers across Japan. 

 The TUS Collaboration: Merging Perception with Human-Centric RL 

The primary research component of this trip was our collaboration with the Interactive Robotics Lab (Yoshida Lab) at TUS. TUS aims to make robots more intelligent by understanding human behaviour, utilising digital twins to simulate environments and humanoid robots to validate theories of natural motion. 

Figure: Two robotic platforms used for research at the Interactive Robotics Lab at TUS. Left: The Kaleido humanoid attached with tactile skin sensors. Right: Dual arm industrial robot

 

Our research streams are complementary where TUS has directed their research toward human-centric robotics, human-understanding and machine learning, and the team at the Australian Cobotics Centre focuses on cobots, perception, and how to intelligently construct and utilise representations of the environment. Our shared goal was to integrate our proposed research directly into TUS’s frameworks to create smarter, more efficient robot behaviours. 

During our time in the lab, we focused on three interconnected challenges in cobot manipulation: 

First, we looked at spatial awareness. We wanted to explore how continuous geometric information, specifically EDFs could be integrated directly into learning frameworks to give robots a better understanding of their environment and bridge the gap between simulation and reality. 

Second, we addressed the complexity of redundant manipulators. When a robot has an infinite number of ways to move its arm to reach a target, calculating the most efficient, collision-free path is computationally expensive. We wanted to explore a better way to map these possibilities and find a suitable trajectory. 

Finally, we challenged the traditional approach to clutter. Standard perception systems treat obstacles as strict “no-go” zones. However, humans use the natural redundancy of their arms to gently push minor obstacles aside while reaching for a goal. We aimed to apply this physical intuition to cobots, allowing them to safely interact with their environment rather than simply avoiding it. 

Outcomes 

This collaboration established an ongoing research pipeline between our institutions, resulting in three joint publications that advance cobot planning and motion: 

Bridging the Sim-to-Real Gap (International Conference on Intelligent Robots and Systems, IROS): We developed an RL framework that uses Euclidean Distance Fields (EDF) directly as the robot’s observation space. This gives the agent continuous geometric information about its environment, allowing for “zero-shot” transfer from simulation to a physical 7-DoF redundant manipulator. The real robot successfully generalised to unseen environments and avoided dynamic obstacles in real-time, despite being trained only on static objects. 

Figure: A robot with surrounding obstacles (green balls). Using the camera on the left of the setup, a visualisation of the environment is generated where warmer colours indicate proximity to an obstacle 

 

Leveraging redundancy (Robotics: Science and Systems, RSS): We explored methods for leveraging manipulator redundancy to execute complex paths. Using efficient null-space sampling, we mapped the configuration space manifold for specific trajectories in the task space. This enabled us to create a distance field in configuration space, providing the robot with a highly efficient way to calculate collision-free trajectories. 

Figure: This image shows the infinite number of arm configurations the robot can be in whilst maintaining the same tool location and orientation.  

Whole-Arm Safe Push and Place: In a forthcoming journal publication, we propose an alternative to strict obstacle avoidance. We designed a system that allows a cobot to hold an object in its end-effector while using its intermediate arm links to push obstacles out of the way. By using environmental information from the EDF with tactile feedback and enabling the robot to dynamically adjust its task speed during contacts, the robot can safely clear its own path, mimicking human behaviour. 

Figure: A robot using its elbow and forearm to push a box out of its way so it can complete its designated task. 

 

Looking Ahead 

These outcomes highlight the value of international collaboration. By combining TUS’s expertise in RL and human-centric robotics with our focus on perception and cobots, we addressed complex challenges that would be difficult to solve independently. The insights and frameworks developed during this trip continue to inform our ongoing research at the Australian Cobotics Centre. 

About the author

Sheila Sutjipto is a researcher at the UTS Robotics Institute. Her research interests are in physical human-robot interaction with a focus on intelligent manipulator control schemes. She has worked on various industry-partnered robotics research projects, and has academic teaching and course develop ... more