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A Robotaxi prototype developed by TIER IV. PHOTO: TIER IV
Inside a partnership showing how open-source platforms and startups are scaling autonomous driving beyond the lab.
Updated
December 17, 2025 2:52 PM
A Robotaxi prototype developed by TIER IV. PHOTO: TIER IV
Autonomous driving is often discussed in terms of futuristic cars and distant timelines. This investment is about something more immediate. Japan-based TIER IV has invested in Turing Drive, a Taiwan startup that builds autonomous driving systems designed for controlled, everyday environments such as factories, ports, airports and industrial campuses. The investment establishes a capital and business alliance between the two companies, with a shared focus on developing autonomous driving technology and expanding operations across Asia.
Rather than targeting open roads and city traffic, Turing Drive’s work centres on places where vehicles follow fixed routes and move at low speeds. These include logistics hubs, manufacturing facilities and commercial sites where automation is already part of daily operations. According to the release, Turing Drive has deployments across Taiwan, Japan and other regions and works closely with vehicle manufacturers to integrate autonomous systems into special-purpose vehicles.
The investment also connects Turing Drive more closely with Autoware, an open-source autonomous driving software ecosystem supported by TIER IV. Turing Drive joined the Autoware Foundation in September 2024 and develops its systems using this shared software framework. TIER IV’s own Pilot.Auto platform, which is built around Autoware, is used across applications such as factory transport, public transit, freight movement and autonomous mobility services.
Through the alliance, TIER IV plans to work with Turing Drive to further develop autonomous driving systems for these controlled environments, while strengthening its presence in Taiwan and the broader Asia-Pacific region. The collaboration brings together software development and on-the-ground deployment experience within markets where autonomous driving is already being tested in real operational settings.
“This partnership with Turing Drive represents a significant step forward in accelerating the deployment of autonomous driving across Asia”, said TIER IV CEO Shinpei Kato. “At TIER IV, our mission has always been to make autonomous driving accessible to all. By collaborating with Turing Drive, which has demonstrated remarkable achievements in real-world deployments in Taiwan, we aim to deliver autonomous driving that enables a safer, more sustainable and more inclusive society”.
“We are thrilled to establish this strategic alliance with TIER IV, a global leader in open-source autonomous driving”, said Weilung Chen, chairman of Turing Drive. “In Taiwan, autonomous driving deployment is gaining significant momentum, particularly across logistics hubs, ports, airports and industrial campuses. By combining our field expertise with TIER IV's world-class Pilot.Auto platform, we aim to accelerate the development of practical, commercially viable mobility services powered by autonomous driving”. Overall, the investment highlights how autonomous driving in Asia is being shaped by operational needs and gradual integration, rather than headline-grabbing demonstrations.
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Robots that learn on the job: AgiBot tests reinforcement learning in real-world manufacturing.
Updated
November 27, 2025 3:26 PM
A humanoid robot works on a factory line, showcasing advanced automation in real-world production. PHOTO: AGIBOT
Shanghai-based robotics firm AgiBot has taken a major step toward bringing artificial intelligence into real manufacturing. The company announced that its Real-World Reinforcement Learning (RW-RL) system has been successfully deployed on a pilot production line run in partnership with Longcheer Technology. It marks one of the first real applications of reinforcement learning in industrial robotics.
The project represents a key shift in factory automation. For years, precision manufacturing has relied on rigid setups: robots that need custom fixtures, intricate programming and long calibration cycles. Even newer systems combining vision and force control often struggle with slow deployment and complex maintenance. AgiBot’s system aims to change that by letting robots learn and adapt on the job, reducing the need for extensive tuning or manual reconfiguration.
The RW-RL setup allows a robot to pick up new tasks within minutes rather than weeks. Once trained, the system can automatically adjust to variations, such as changes in part placement or size tolerance, maintaining steady performance throughout long operations. When production lines switch models or products, only minor hardware tweaks are needed. This flexibility could significantly cut downtime and setup costs in industries where rapid product turnover is common.
The system’s main strengths lie in faster deployment, high adaptability and easier reconfiguration. In practice, robots can be retrained quickly for new tasks without needing new fixtures or tools — a long-standing obstacle in consumer electronics production. The platform also works reliably across different factory layouts, showing potential for broader use in complex or varied manufacturing environments.
Beyond its technical claims, the milestone demonstrates a deeper convergence between algorithmic intelligence and mechanical motion.Instead of being tested only in the lab, AgiBot’s system was tried in real factory settings, showing it can perform reliably outside research conditions.
This progress builds on years of reinforcement learning research, which has gradually pushed AI toward greater stability and real-world usability. AgiBot’s Chief Scientist Dr. Jianlan Luo and his team have been at the forefront of that effort, refining algorithms capable of reliable performance on physical machines. Their work now underpins a production-ready platform that blends adaptive learning with precision motion control — turning what was once a research goal into a working industrial solution.
Looking forward, the two companies plan to extend the approach to other manufacturing areas, including consumer electronics and automotive components. They also aim to develop modular robot systems that can integrate smoothly with existing production setups.