Robotics Controls & Teleoperation Engineer

About AIRoA AI Robot Association (AIRoA) is an organization dedicated to collecting large-scale real-world robot data, including data from humanoid robots, and advancing the development of foundation models for generative AI in the field of robotics. AIRoA has been selected as an implementing organization for the development of a data platform for generative AI foundation models in robotics under the “Post-5G Information and Communication Systems Infrastructure Enhancement R&D Project” by the Ministry of Economy, Trade and Industry (METI) and NEDO. The project budget is JPY 20.5 billion. Based on this foundation, AIRoA is pursuing a project to collect humanoid robot operation data at a scale of one million hours using more than 100 robots, and to develop a world-class Vision-Language-Action (VLA) model utilizing this data. Responsibilities Teleoperation Platform: Design and implement teleoperation systems for humanoid robots, mobile manipulators, and service robots, and continuously improve usability, stability, latency, and recovery performance. Real-Robot Control and Assistive Control: Implement control modules, state management, safety stops, operation mode switching, sensor synchronization, and interfaces with actuator control for real robots using ROS/ROS2. Demo Collection Quality: Improve the quality of human demonstrations used for imitation learning and VLA evaluation by developing operation logs, sensor logs, failure classifications, reproduction procedures, and data collection workflows. Cross-Functional Collaboration: Work with the Autonomy, VLA, Simulation, Integration, and Hardware teams to ensure that teleoperation, control, testing, and real-robot evaluation operate consistently as an integrated system. Real-Robot Debugging: Analyze low-latency communication, control cycles, sensor synchronization, abnormal states, and recovery behavior from logs, and reproduce, fix, and verify issues on real robots. Required Qualifications Experience in real-robot control or real-robot debugging with mobile robots, manipulators, humanoid robots, industrial robots, service robots, or similar systems. Experience implementing robot control, real-time systems, communication, log analysis, or related tools in C++ or Python. Experience building robot systems using ROS or ROS2, and performing system integration or system analysis for real-robot systems. Ability to develop with an understanding of interfaces across multiple modules, such as sensors, control, state management, safety stops, UI, data collection, and evaluation environments. Ability to persistently address low-reproducibility issues that occur on real robots through logs, hypotheses, reproduction experiments, fixes, and verification. Preferred Qualifications Experience with teleoperation, such as VR, haptics, force feedback, leader-follower systems, motion capture, puppeteering-based control, or real-time retargeting. Experience with advanced control, such as impedance control, admittance control, force/torque control, MPC, whole-body control, or dexterous manipulation. Experience with learning-based control, such as imitation learning, reinforcement learning, hybrid MPC + learning, safety-constrained learning, or learned controller deployment. End-to-end experience with data collection, including human demonstration collection, operation quality evaluation, failure classification, task specification, evaluation set construction, and collaboration with VLA or robot learning teams. Experience with real-robot operations, such as remote operation, semi-autonomous operation, multi-robot operation, field testing, or long-duration operation testing. Experience with fleet management or product-level system operations.