Research

I develop optimization-based motion planning and control algorithms for safe and robust physical autonomy in uncertain (dynamic and partially-observed) environments. My research integrates stochastic optimization, risk-sensitive distributional reinforcement learning (DRL), and safety-critical control using methods such as quadratic optimization and stochastic model predictive control (MPC) to improve decision-making for embodied, intelligent, and self-governing (single or multi-robot) systems. As autonomous systems make their way into more and more human environments, my research is also devoted (in part) to the design of uncertainty-aware decision-making algorithms that not only guarantee satisfactory performance on prescribed task metrics but also ensure robustness and computational efficiency.

Besides theoretical and algorithmic contributions, I also implement and validate my work in high-fidelity simulators and learning sandboxes (Isaac Sim, Gazebo, OpenAI Gym, Safety Gymnasium), and more recently, real robotic hardware, leveraging several programming, numerical optimization, machine learning, and robotics tools and technologies, including C++, Pinocchio, TensorFlow, PyTorch, Gurobi, Pyomo, Ipopt, CVXPY, and ROS(2).

Demonstration of Risk-Aware QR-DQN in action

Risk-Aware DRL

Hand IK Demo

Any-Chain Trajectory Planning

Safety-Critical Control Demo

Robot Control

Multi-Quadrotor Control Demo

Multi-Robot Coordination

Publications

Peer-Reviewed
2025

CDC
Clinton Enwerem, Aniruddh G. Puranic, John S. Baras, and Calin Belta, Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Value Iteration and Quantile Regression. To appear in the proceedings of the 64th IEEE Conference on Decision and Control (CDC), 2025.
2024

CDC
Clinton Enwerem, Erfaun Noorani, John S. Baras, and Brian M. Sadler, Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling, In the proceedings of the 63rd IEEE Conference on Decision and Control (CDC), 2024.
ECC
Clinton Enwerem and John S Baras. Safe Collective Control under Noisy Inputs and Competing Constraints via Non-Smooth Barrier Functions. In the 2024 European Control Conference (ECC), pp. 3762–3768. IEEE, 2024.
LCSS
Clinton Enwerem and John S. Baras, Formation Tracking for a Class of Uncertain Multiagent Systems: A Distributed Kalman Filtering Approach, IEEE Control Systems Letters, Volume 8, 2024.

2023

CoDIT
Clinton Enwerem and John S. Baras, "Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent Systems," in the 9th International Conference on Control, Decision and Information Technologies (CoDIT), Rome, Italy: IEEE, Jul. 2023, pp. 1226–1231. doi: 10.1109/CoDIT58514.2023.10284199.
2023

arXiV
Clinton Enwerem, John S. Baras, and Danilo Romero, "Distributed Optimal Formation Control for an Uncertain Multiagent System in the Plane," arXiv:2301.05841 [cs, eess], Jan. 2023.