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About

Michael Bombile is a leading researcher in dynamic robotic manipulation and bipedal locomotion, whose work is redefining how robots interact with their environment through speed and coordination. His primary research areas include bimanual control, dynamic object handling, and reactive walking. Bombile’s major contributions center on enabling robots to perform complex, human-like tasks such as coordinated grabbing and tossing of objects onto moving targets—a capability critical for industrial automation. His 2022 paper on dual-arm control for fast grabbing and tossing has garnered 39 citations, while his 2023 work on bimanual tossing onto moving targets (12 citations) and his 2021 study on learning to hit objects (16 citations) further demonstrate his impact. Notably, Bombile has developed a unified motion generator for bimanual robotic systems and a statistical dynamical system for object manipulation after impact, pushing the boundaries of what robots can achieve outside their immediate workspace. Additionally, his 2017 paper on capture-point based balance and reactive omnidirectional walking (7 citations) showcases his versatility in locomotion control, proposing a Model Predictive Control framework that integrates Center of Mass and Capture Point dynamics for stable, adaptive walking. Bombile’s innovative approach is paving the way for more agile, responsive robots in both industrial and service settings.