Prem Chand
Papers
2
Total Citations
9
H-Index
2
About
Prem Chand is a roboticist advancing the frontier of dynamic bipedal locomotion, with a focus on adaptive control and human-robot physical interaction. His work centers on enabling walking robots to navigate uncertainty—whether from unknown terrain, parametric variation, or direct human guidance. In his most cited work, “Interactive Dynamic Walking: Learning Gait Switching Policies With Generalization Guarantees” (2022, 6 citations), Chand introduces a supervisory framework that orchestrates switching among Dynamic Movement Primitives (DMPs), allowing a bipedal robot to physically follow a human coworker with provable generalization. This bridges reinforcement learning and control theory for safer, more responsive co-robots. Earlier, in “An Adaptive Supervisory Control Approach to Dynamic Locomotion Under Parametric Uncertainty” (2020, 3 citations), he developed an online supervisor that uses logic-based switching among a finite set of controllers to identify and adapt to large structured uncertainties in real time—a critical step toward robots that can operate reliably outside lab conditions. Chand’s contributions are shaping the next generation of legged robots that walk not just with stability, but with adaptability.
Research Focus
Key Achievements
Top Papers
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