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About

Jonas C. Kiemel is a robotics researcher whose work sits at the intersection of machine learning and robot motion planning, with a particular focus on safe, adaptive trajectory generation for robotic systems. His research addresses one of the field's most pressing challenges: enabling robots to move efficiently and safely in dynamic, real-world environments while respecting physical constraints such as torque limits, joint velocities, acceleration, and jerk boundaries. Kiemel's most cited contribution, "Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors" (2022, 8 citations), introduced a neural network-driven framework for online trajectory generation that maintains safety guarantees during robot operation. His earlier work, TrueÆdapt (2020, 4 citations), demonstrated model-free online trajectory adaptation using sensory feedback — a meaningful step toward responsive, environment-aware robotics. He has further extended these ideas to handle moving obstacles through safe reinforcement learning techniques (2024, 4 citations), and explored time-optimized path tracking that functions with or without sensory input (2022, 3 citations). Though early in his research career, Kiemel has established a coherent and practically impactful body of work, contributing foundational methods that bridge the gap between theoretical safety constraints and deployable, learning-based robot control.