Yaru Chen
Papers
3
Total Citations
32
H-Index
3
About
Yaru Chen is a rising researcher in the fields of neuromusculoskeletal robotics and biologically inspired motor control. Their work focuses on bridging the gap between biological motor learning and robotic limb control, with key contributions in muscle synergy identification and reinforcement learning for musculoskeletal systems. Chen’s most cited paper (2023, 21 citations) introduces a novel neuromuscular control method combining proximal policy optimization with time-varying muscle synergies for upper limb musculoskeletal robots, addressing the longstanding challenge of motion learning in these complex systems. Their 2024 work (6 citations) develops a computational method to identify optimal functional muscle synergies from estimated activations, overcoming limitations of traditional EMG-based approaches. Additionally, Chen’s 2020 paper (5 citations) proposes a biologically constrained cerebellar model integrating reinforcement learning for robotic limb control, challenging the traditional view of the cerebellum as purely a supervised learning structure. These contributions collectively advance the development of more adaptable, robust, and human-like robotic systems, with potential applications in rehabilitation robotics and prosthetics.
Research Focus
Key Achievements
Top Papers
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