- Operation and Control
Two-stage control learning technology for image-based control
Reduces work in the application of controls and systems through end-to-end learning that does not require design of image recognition processing.
- Learns policy for controlling robots from input images.
- Reduces labor by learning control policy from accumulated operation data by experts or simulation environments.
- Achieves high accuracy control by learning a control policy and a policy for correcting the control simultaneously.
Applications
- Automation systems in logistics, manufacturing, maintenance, and inspection facilities that use robots
- Automated control in moving bodies (e.g., AGVs and drones)
- Control assistance for defense, medical devices, etc.
Benchmarks, strengths, and track record
- The control policy is not susceptible to the surrounding environments, and can be easily learned from simulation environments.
- When simulation environments are not available, control policy can be learned from accumulated operations data by experts.
Inquiries
Contact the Toshiba Corporate Research & Development Center
Please include the title “Toshiba AI Technology Catalog: Two-stage control learning technology for image-based control” or the URL in the inquiry text.
Please note that because this technology is currently the subject of R&D activities, immediate responses to inquiries may not be possible.
References:
- Q. Dong, et al., "An Offline Learning of Behavior Correction Policy for Vision-Based Robotic Manipulation", In 41st IEEE Conference on Robotics and Automation (ICRA 2024).
- Toshiba developed the top precise control of complex robot operations using ‘offline reinforcement learning’ with a small amount of data -training AI safely and efficiently for promoting automation in manufacturing sites, contributing to the alleviation of labor shortages-. R&D Center; Toshiba (global.toshiba)
