Venkatraman Renganathan

Venkatraman Renganathan

Postdoctoral Fellow - Dept. of Automatic Control - LTH, Lund University

Learning based & Risk Bounded Motion Planning

Guaranteeing safe motion plans for robots is very important during critical robotic missions. My earlier research involved about coming up with motion plans that will work for uncertainties affecting the robot from any distribution satisfying first two moments. My current research is about investigating different non-uniform risk allocation procedures and design of learning based motion planning for robots in uncertain environments.

  1. V. Renganathan, S. Safaoui, A. M. Kothari, B. Gravell, I. Shames, T. Summers, Risk Bounded Nonlinear Motion Planning With Integrated Perception & Control, Special Issue on Risk-aware Autonomous Systems: Theory and Practice, Artificial Intelligence, 2023.
  2. K. Ekenberg, V. Renganathan, B. Olofsson, Distributionally Robust RRT with Risk Allocation , Accepted to IEEE ICRA, 2023.
  3. C. Alpturk, V. Renganathan, Risk Averse Path Planning Using Lipschitz Approximated Wasserstein Distributionally Robust Deep Q-learning, ECC, Bucharest, Romania, 2023.