Samarth Chopra
Papers
2
Total Citations
6
H-Index
2
About
Samarth Chopra is a rising researcher at the intersection of neuromorphic computing and autonomous robotics, specializing in bio-inspired locomotion and event-based sensorimotor control. His work focuses on designing efficient neural architectures that enable miniature robots to navigate complex, unstructured environments under severe resource constraints. Chopra’s major contributions include the development of tunable bursting rhythms using spiking central pattern generators (CPGs) implemented on Loihi neuromorphic chips and Arduino platforms—demonstrating how biological principles of rhythmic movement can be translated into robust robot locomotion. His most-cited paper (2023, 4 citations) pioneers this approach, while his 2025 work advances autonomous navigation by integrating supervised gait learning with event-based obstacle avoidance, addressing critical challenges in disaster response and remote exploration. Though early in his career, Chopra’s research is notable for bridging theoretical neuroscience with practical edge robotics, offering scalable solutions for low-power, real-time control. His work holds promise for next-generation autonomous systems that must operate without constant supervision, making him a key voice in the growing field of neuromorphic robotics.
Research Focus
Key Achievements
Top Papers
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- 2