Maks Sorokin
Papers
7
Total Citations
76
H-Index
4
About
Maks Sorokin is a robotics researcher whose work sits at the intersection of quadrupedal locomotion, deep reinforcement learning, and autonomous navigation. His most impactful contribution is the development of a quadruped robot capable of navigating urban sidewalks using public map services—a key enabler for last-mile delivery and neighborhood patrol applications. This work, "Learning to Navigate Sidewalks in Outdoor Environments," has garnered 35 citations and demonstrates his ability to bridge simulation and real-world deployment. Sorokin has also pioneered human motion-based control interfaces for non-humanoid robots, allowing operators to intuitively guide quadrupeds through hazardous environments via deep reinforcement learning (15 and 9 citations). His research extends to meta-learning for visual navigation, enabling robots to adapt to new environments with minimal data, and to self-supervised approaches for analyzing complex, multi-timescale behaviors. Notably, Sorokin has explored how robot morphology itself can be optimized to enhance learning and task performance, reflecting a holistic view of robot design. With a growing citation record and a focus on practical, deployable systems, Sorokin is shaping the future of legged robots that work alongside humans in unstructured, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Learning to Navigate Sidewalks in Outdoor Environments35 citations · 2022
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- 7Learning to Navigate Sidewalks in Outdoor Environments2 citations · 2021