Jacky Baltes

This study presents a comprehensive Open Innovation model, infused with Systems Thinking principles, to investigate the FIRA Innovation & Business League’s role as a global platform for propelling robotics and emerging technologies forward. By framing the league as a multi-stakeholder ecosystem, the research delineates a layered framework that elucidates interactions among startups, established organizations, and the wider community. Employing a mixed-methods approach, the study illuminates how the league enables knowledge exchange, technology transfer, and entrepreneurial collaboration. The findings underscore that this integrated model accelerates innovation cycles, bolsters startup performance, and cultivates robust linkages among industry, academia, and government. A SWOT analysis further unveils the challenges and opportunities inherent in maintaining open innovation amid a swiftly evolving technological terrain. The paper culminates in underscoring the pivotal role of problem-driven innovation formats and sustainable ecosystem architectures, with special attention to the league’s forthcoming iterations, including its anticipated 2026 expansion into Canada.

Amirmahdi Zarif, Amirmohammad Zarif Shahsavan Nejad, Jacky Baltes, Kuo-Yang Tu, Soroush Sadeghnejad Verified by AVIS CertificateFIRA World Summit 2026Submitted 17 Sept 2026

Self-balancing wheeled robots present unique challenges for mobile manipulation due to continuous variations in height, orientation, and tilt caused by the robot’s self-balancing dynamics. In this paper, we present a PPO-based framework with a five-stage dense reward function that shapes the policy to reach, grasp, lift, transport, and accurately place objects on our self-balancing two-wheeled robot under a generalized multiobject training setting, where all three objects (Mug, Drill, and Dumbbell) are trained simultaneously in a single policy. The fivestage reward guides exploration in this high-dimensional floatingbase manipulation task without requiring explicit kinematic programming. Our policy achieves a success rate of 69.78% on the Drill task and sub-centimeter placement accuracy of 9.52 mm. The experiments also reveal the spontaneous emergence of prealignment behavior, where the robot learns to reorient asymmetric objects into graspable poses without explicit instruction, suggesting that the reward structure encourages adaptive manipulation strategies beyond what was explicitly programmed.

Shi-Han Wang, Hanjaya Mandala, Saeed Saeedvand, Jacky Baltes Verified by AVIS CertificateFIRA World Summit 2026Submitted 17 Sept 2026