Utsav Patel
Papers
12
Total Citations
399
H-Index
8
About
Utsav Patel is a robotics researcher whose work sits at the intersection of deep reinforcement learning, autonomous robot navigation, and human-aware robotics. His research has made significant strides in enabling robots to navigate safely and intelligently across diverse and challenging environments — from dense pedestrian crowds to uneven outdoor terrains. Patel's most impactful contributions include DWA-RL (75 citations), which combines classical robot dynamics constraints with deep reinforcement learning for mobile obstacle avoidance, and TERP (67 citations), a navigation system for reliable outdoor terrain traversal using elevation-aware attention mechanisms. His Crowd-Steer work addressed the critical sim-to-real gap in crowd navigation by developing high-fidelity simulation environments that model realistic pedestrian behavior, friction, and sensor noise. During the COVID-19 pandemic, Patel demonstrated the real-world applicability of his research by developing robotic systems capable of autonomously monitoring social distancing compliance in crowded indoor settings, garnering over 130 combined citations across two related publications. His more recent GrASPE framework extends this vision further, fusing multimodal sensory data through graph-based learning for complex outdoor planning. Collectively, Patel's body of work — exceeding 395 total citations — reflects a consistent commitment to making autonomous robots safer, smarter, and more socially aware.
Research Focus
Key Achievements
Top Papers
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- 4COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios55 citations · 2020
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