What is Imitation Learning? How to Collect Data to Teach Robots Tasks, and Real-World Examples in Japan and Abroad [2026 Edition]
What is Imitation Learning? How to Collect Data to Teach Robots Tasks, and Real-World Examples in Japan and Abroad [2026 Edition]
This tutorial explains imitation learning as a method for robots to learn tasks from human demonstrations. It outlines three data collection methods (teleoperation, direct teaching, observation), reviews global examples including Toyota Research Institute's Diffusion Policy approach using dozens of demonstrations and Physical Intelligence's pi-zero model trained on approximately 10,000 hours of data, and details the AI Robot Association's release of the AIRoA MoMa 5k dataset (approximately 5,000 hours) in July 2026. The text provides five steps for field data collection and five principles for data design to avoid failure, emphasizing that success depends on consistent data systems rather than just AI models.
The article explains imitation learning methods and data collection strategies, citing specific initiatives from Toyota Research Institute, Physical Intelligence, and the AI Robot Association with stated data volumes and project timelines.
Source: Robotics — Robot learning news discovery · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
robot motion data hours
≈5,000 hours
View original evidence
Conclusion of this article (a 30-second summary) Imitation learning is a method for robots to learn tasks from human demonstrations . Toyota's research institute enabled a robot to learn over 60 skills using only a few dozen demonstrations. In Japan, the AI Robot Association released approximately 5,000 hours of robot motion data to the public in July 2026. What determines success is not the AI model, but a system fo
Open source S2foundation model data hours
10,000 hours
View original evidence
a few dozen demonstrations ( The Robot Report ) Key Takeaway : The number of demonstrations can start from "dozens" rather than "thousands." Also, it is important that they use not only video but also haptic data. Example 2: Physical Intelligence (USA) | A foundation model that handles daily tasks with 10,000 hours of data What they did : The US startup Physical Intelligence developed the robot foundation model "π0 (
Open source S10future dataset collection goal hours
100,000 hours
View original evidence
the robot motion dataset "AIRoA MoMa 5k" (approximately 5,000 hours), the robot foundation model learned from it, and the learning source code. This is a result of the GENIAC project adopted by the Ministry of Economy, Trade and Industry and NEDO ( ExaWizards ) Future plans : In the GENIAC project, they aim to build a 100,000-hour scale dataset using more than 35 standard machines ( METI GENIAC Document ). Furthermor
Open source S13future humanoid operation data collection goal hours
1,000,000 hours
View original evidence
e, the association's job postings indicate a plan to collect 1 million hours of humanoid operation data using over 100 robots ( BizReach Job Posting ) Key Takeaways : A trend has begun in Japan to collect and publish learning data across industries that a single company could not gather on its own. We are entering an era where even small and medium-sized enterprises can experiment by adding a small amount of their ow
Open source S14robot skills acquired
>60
Reported trials
View original evidence
eaching robots skills using "Diffusion Policy," a type of generative AI. The teacher provides demonstrations while feeling haptic feedback and also conveys goals through language ( TRI ) Results : They enabled the robot to acquire over 60 skills, such as pouring liquids, using tools, and handling soft objects, using only data without writing new code. They state that a single skill can be autonomously executed after
Open source S9robots for future data collection
>100 robots
View original evidence
e, the association's job postings indicate a plan to collect 1 million hours of humanoid operation data using over 100 robots ( BizReach Job Posting ) Key Takeaways : A trend has begun in Japan to collect and publish learning data across industries that a single company could not gather on its own. We are entering an era where even small and medium-sized enterprises can experiment by adding a small amount of their ow
Open source S14
- paper: not_reported
- dataset: Reported pending Open source S13
- code: Reported pending Open source S13
- dataset: not_reported
- code: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
Conclusion of this article (a 30-second summary) Imitation learning is a method for robots to learn tasks from human demonstrations . Toyota's research institute enabled a robot to learn over 60 skills using only a few dozen demonstrations. In Japan, the AI Robot Association released approximately 5,000 hours of robot motion data to the public in July 2026. What determines success is not the AI model, but a system fo
Open source S2
the robot motion dataset "AIRoA MoMa 5k" (approximately 5,000 hours), the robot foundation model learned from it, and the learning source code. This is a result of the GENIAC project adopted by the Ministry of Economy, Trade and Industry and NEDO ( ExaWizards ) Future plans : In the GENIAC project, they aim to build a 100,000-hour scale dataset using more than 35 standard machines ( METI GENIAC Document ). Furthermor
Open source S13
a few dozen demonstrations ( The Robot Report ) Key Takeaway : The number of demonstrations can start from "dozens" rather than "thousands." Also, it is important that they use not only video but also haptic data. Example 2: Physical Intelligence (USA) | A foundation model that handles daily tasks with 10,000 hours of data What they did : The US startup Physical Intelligence developed the robot foundation model "π0 (
Open source S10
e, the association's job postings indicate a plan to collect 1 million hours of humanoid operation data using over 100 robots ( BizReach Job Posting ) Key Takeaways : A trend has begun in Japan to collect and publish learning data across industries that a single company could not gather on its own. We are entering an era where even small and medium-sized enterprises can experiment by adding a small amount of their ow
Open source S14
eaching robots skills using "Diffusion Policy," a type of generative AI. The teacher provides demonstrations while feeling haptic feedback and also conveys goals through language ( TRI ) Results : They enabled the robot to acquire over 60 skills, such as pouring liquids, using tools, and handling soft objects, using only data without writing new code. They state that a single skill can be autonomously executed after
Open source S9
Source：Robotics — Robot learning news discovery · note.com