Max Robotics — Global Service Robot Directory + US Certification

About

Pitoyo Hartono is a robotics and artificial intelligence researcher whose work spans reinforcement learning, autonomous robot control, and intelligent systems. Over more than two decades, he has made consistent contributions to some of the most challenging problems in robotics: teaching machines to learn efficiently, move intelligently, and operate independently in complex real-world environments. His early contributions, including work presented at the prestigious IEEE-RAS Humanoids conference (55 citations) and research on double inverted pendulum control using nested actor-critic algorithms, helped lay groundwork for practical reinforcement learning in physical robotic systems. His 2009 paper on fast reinforcement learning for simple physical robots demonstrated a commitment to making these techniques accessible and deployable beyond simulation. More recently, Hartono has pushed into multi-robot coordination, hierarchical deep reinforcement learning, and biologically inspired robotics — including controlling bionic robotic fish using shape memory alloy actuators. Notably, his work on visualizing the internal representations of learning robots reflects a growing interest in interpretability and transparency within AI-driven systems. With research spanning humanoid robots, survival robots in outdoor environments, and common-sense knowledge integration, Hartono's career represents a thoughtful, wide-ranging exploration of how robots can learn, adapt, and ultimately be understood by the humans who build them.