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About

Denis Hadjivelichkov is a roboticist whose research sits at the intersection of mobile manipulation, deep learning, and reinforcement learning, with a focus on enabling robots to operate intelligently and safely in unstructured environments. His most impactful work, "Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control" (34 citations), presents an integrated system that combines deep learning-based garbage classification with whole-body control for autonomous waste sorting—a practical contribution to sustainable robotics. He has also advanced path planning for legged robots through "ViT-A*: Legged Robot Path Planning using Vision Transformer A*" (9 citations), integrating transformer architectures with classical planning. In the domain of reinforcement learning, Hadjivelichkov has tackled multi-stage task learning via solution sketches and proposed methods for safe trajectory sampling in model-based RL. His recent work on sensorimotor learning with stability guarantees (2025) addresses the critical challenge of ensuring safe, interpretable robot behaviors. With a growing citation record and contributions spanning manipulation, navigation, and learning, Hadjivelichkov is establishing himself as a researcher dedicated to making robots more capable, efficient, and safe in real-world settings.