Papers
3
Total Citations
51
H-Index
3
About
Artem Kovalev is a robotics researcher whose work focuses on intelligent navigation and locomotion for autonomous systems. His primary research areas include reinforcement learning for robotic navigation, bipedal locomotion dynamics, and sensor-based decision-making in stochastic environments. Kovalev’s most impactful contribution is his 2019 paper on applying reinforcement learning to ground robotic platform navigation in both static and dynamically generated indoor environments, which has garnered 38 citations. This work innovatively integrates neural networks with the Unity ML software suite to model complex environments and optimize track routing and movement logistics. He further advanced the field by developing walking patterns for biped robots using the dynamics of a 3D linear inverted pendulum, a foundational approach for stable gait generation. Additionally, his research on robot navigation systems in stochastic environments leverages reinforcement learning on LiDAR data, demonstrating practical applications for real-world uncertainty. Kovalev’s contributions are notable for bridging simulation-based training with real-world robotic autonomy, offering scalable solutions for logistics and service robotics. His work continues to influence researchers developing intelligent, adaptive robotic systems for complex indoor settings.
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