Oct 03, 2024
- Company
- Press Release
Oct 02, 2024
Company / Press Release
Osaka, Japan, October 2, 2024 – Panasonic Holdings Co., Ltd. (Panasonic HD) has developed a diffusion model for robot control it has dubbed the "Diffusion Contact Model," which improves the efficiency and accuracy of robot control learning for contact-rich tasks.
As labor shortages become a serious social issue in many parts of the world, the use of industrial robots is progressing in a number of fields. However, in fields such as the manufacturing and service industries there are many tasks that involve contact with people and objects, yet movements and forces that occur when a robot comes into contact with a person or object are extremely complex and difficult to model in a simulation environment. In order to achieve safe and accurate operation in these situations, it is necessary to conduct multiple trial and error tests in advance using actual machines. To address this issue, which increases the cost and time required to introduce robots into these fields, Panasonic HD has developed an AI technology for robot control, the Diffusion Contact Model, which applies the "Diffusion Model" often used in image generation to robot learning.
This technology has been accepted for presentation at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024, a top conference for AI and robotics technology. Panasonic HD’s Diffusion Contact Model technology will be presented at the conference, set to be held in Abu Dhabi, UAE from October 14 to 18, 2024.
To enable a robot to perform safe and accurate movements, the parameters that control the robot's movements and the amount of force must be finely tuned to suit the situation the robot is in. Tuning methods are mainly divided into simulation environments (model-based) and trial and error using the actual machine (machine-based), with the model-based approach featuring the advantage of being able to carry out a large amount of trial-and-error simulations more efficiently than a machine-based approach. However, tasks that involve contact are difficult to simulate due to the complexity of the movements and forces (contact dynamics) that occur when a robot touches a person or object, and it is necessary to carry out trial and error many times when training a robot for these tasks, and the training also must involve human input.
Therefore, Panasonic HD focused on the features of diffusion models now being used for image and sound generation, as they can express complex, nonlinear models.
Applying the similarity between the noise removal process of the diffusion model and the optimization process of contact simulation, Panasonic HD developed its Diffusion Contact Model that can simulate complex contact dynamics without using an actual machine. The Diffusion Contact Model simulates in stages the force exerted when a robot touches an object , and can predict the force exerted when the robot touches an object with high accuracy, enabling efficient tuning of control parameters on a model basis.
In the conventional method shown in the upper portion of Figure 1, the control parameters are first estimated using a Bayesian optimization algorithm, then evaluated on an actual machine and the parameters are adjusted again, repeating a trial-and-error loop until the desired performance is obtained. On the other hand, in the Diffusion Contact Model shown in the lower portion of the figure, the control parameters are first estimated using a Bayesian optimization algorithm as in the conventional method, but the Diffusion Contact Model is used instead of an actual machine for evaluation.
First, Panasonic HD conducted experiments in a simulation environment, and found that the Diffusion Contact Model can predict contact forces with high accuracy compared to a conventional deep neural network (DNN). If complex contact dynamics can be simulated without an actual machine, the number of situations requiring an actual machine can be reduced compared to conventional methods. In a demonstration using an actual machine for a wiping task, it was shown that the learning time for the wiping task, which took 80 minutes in total by mainly teaching the robot by hand, can be reduced to about 25 minutes using the Diffusion Contact Model.
The Diffusion Contact Model Panasonic HD has developed is a technology that can significantly reduce the time it takes to train a robot using live machines. In addition to the wipe task mentioned in the paper, there are many other robot tasks that are difficult to simulate and require trial and error on an actual machine. Panasonic HD believes that applying this technology will increase the possibility of efficiently automating many tasks, which will be useful in solving social issues such as labor shortages.
Panasonic HD will strive to continue to accelerate the social implementation of AI and robotics, and promote research and development of AI technologies that contribute to helping our customers in their daily lives and at work.
“A Contact Model based on Denoising Diffusion to Learn Variable Impedance Control for Contact-rich Manipulation”
https://arxiv.org/abs/2403.13221
IROS 2024 official website
https://iros2024-abudhabi.org/
Panasonic × AI website
https://tech-ai.panasonic.com/en/
Panasonic Robotics Hub website
https://tech.panasonic.com/global/robot/index.html
About the Panasonic Group
Founded in 1918, and today a global leader in developing innovative technologies and solutions for wide-ranging applications in the consumer electronics, housing, automotive, industry, communications, and energy sectors worldwide, the Panasonic Group switched to an operating company system on April 1, 2022 with Panasonic Holdings Corporation serving as a holding company and eight companies positioned under its umbrella. The Group reported consolidated net sales of 8,496.4 billion yen for the year ended March 31, 2024. To learn more about the Panasonic Group, please visit: https://holdings.panasonic/global/ |
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