Mohak Bhardwaj
Papers
8
Total Citations
67
H-Index
5
About
Mohak Bhardwaj is a robotics researcher specializing in motion planning, model predictive control, and reinforcement learning, with a focus on making autonomous robots faster, smarter, and more adaptable in real-world settings. His most recognized contribution is STORM (2021), an integrated framework for joint-space Model Predictive Control (MPC) that addresses the computational bottlenecks of sampling-based MPC in high-dimensional robotic manipulation — a paper that has garnered 23 citations and represents a significant step toward reactive, real-time robot control. Bhardwaj has also made notable strides in bridging model-free and model-based reinforcement learning through his Information Theoretic Model Predictive Q-Learning framework, demonstrating how hybrid approaches can overcome the limitations of each paradigm in expensive real-world robotic environments. His earlier work on learning heuristics for search via imitation learning highlights his sustained interest in data-driven planning under computational constraints. His 2020 work on Differentiable Gaussian Process Motion Planning further demonstrates his ability to make trajectory optimization more principled and adaptive. Across his career, Bhardwaj's research reflects a coherent vision: enabling robots to plan and act efficiently, intelligently, and safely alongside humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2Learning Heuristic Search via Imitation12 citations · 2017
- 3Information Theoretic Model Predictive Q-Learning9 citations · 2020
- 4Differentiable Gaussian Process Motion Planning7 citations · 2020
- 5Information Theoretic Model Predictive Q-Learning6 citations · 2019
- 6Data-driven planning via imitation learning5 citations · 2018
- 7Fast Joint Space Model-Predictive Control for Reactive Manipulation.3 citations · 2021
- 8