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 increasingly make their way into 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. To validate my theoretical and algorithmic ideas, I employ high-fidelity physics simulators (Isaac Sim), RL sandboxes (OpenAI Gym and Safety Gymnasium), and real robotic systems, leveraging tools such as Pinocchio, TensorFlow, PyTorch, Mosek, osqp, and ROS 2.

Applications

Results from my research find application in:

  • Robust navigation for mobile robots (ground and aerial) in structured or unstructured environments
  • Dexterous manipulation using robotic manipulators fitted with high-DoF anthropomorphic grippers
  • Distributed and decentralized coordination of mobile multi-robot systems.

Demonstration of Risk-Aware QR-DQN in action

Robot Navigation

Hand IK Demo

Grasp Planning

Safety-Critical Control Demo

Manipulator Control

Multi-Quadrotor Control Demo

Multi-Agent Systems

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.
paper 
@online{enweremSafetyAwareRLforControl2025,
title = {Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Value Iteration and Quantile Regression},
author = {Enwerem, Clinton and Puranic, Aniruddh G. and Baras, John S. and Belta, Calin},
year = {2025},
eprint = {2506.06954},
eprinttype = {arXiv},
eprintclass = {cs.LG},
doi = {10.48550/arXiv.2506.06954},
url = {http://arxiv.org/abs/2506.06954},
urldate = {2025-06-08},
pubstate = {prepublished},
keywords = {Computer Science - Machine Learning,Computer Science - Robotics},
}
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.
paper 
@online{enweremRobustStochasticShortestPath2024b,
title = {Robust {{Stochastic Shortest-Path Planning}} via {{Risk-Sensitive Incremental Sampling}}},
author = {Enwerem, Clinton and Noorani, Erfaun and Baras, John S. and Sadler, Brian M.},
year = {2024},
eprint = {2408.08668},
eprinttype = {arXiv},
eprintclass = {cs},
doi = {10.48550/arXiv.2408.08668},
url = {http://arxiv.org/abs/2408.08668},
urldate = {2024-12-20},
pubstate = {prepublished},
keywords = {Computer Science - Artificial Intelligence,Computer Science - Robotics,Computer Science - Systems and Control,Electrical Engineering and Systems Science - Systems and Control},
}
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.
paper 
@inproceedings{enweremSafeCollectiveControl2024,
title = {Safe {{Collective Control Under Noisy Inputs}} and {{Competing Constraints}} via {{Non-Smooth Barrier Functions}}},
booktitle = {2024 {{European Control Conference}} ({{ECC}})},
author = {Enwerem, Clinton and Baras, John S.},
year = {2024},
pages = {3762--3768},
doi = {10.23919/ECC64448.2024.10591027},
url = {https://ieeexplore.ieee.org/abstract/document/10591027},
urldate = {2024-12-20},
eventtitle = {2024 {{European Control Conference}} ({{ECC}})},
keywords = {control barrier functions,Control systems,multi-agent systems,Polynomials,Robustness,Safety,safety-critical control,Smoothing methods,stochastic model-predictive control,Stochastic processes,Upper bound}
}
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.
paper 
@article{enweremFormationTrackingClass2024a,
title = {Formation {{Tracking}} for a {{Class}} of {{Uncertain Multi-Agent Systems}}: {{A Distributed Kalman Filtering Approach}}},
shorttitle = {Formation {{Tracking}} for a {{Class}} of {{Uncertain Multi-Agent Systems}}},
author = {Enwerem, Clinton and Baras, John S.},
year = {2024},
journaltitle = {IEEE Control Systems Letters},
volume = {8},
pages = {217--222},
issn = {2475-1456},
doi = {10.1109/LCSYS.2024.3364987},
url = {https://ieeexplore.ieee.org/abstract/document/10433084},
urldate = {2024-12-20},
eventtitle = {{{IEEE Control Systems Letters}}},
keywords = {Collective control,distributed Kalman filtering (DKF),Filtering,formation tracking,Kalman filters,leader-follower networks,multi-agent systems,Noise measurement,Robot kinematics,Sensors,Standards,Uncertainty}
}

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.
paper 
@inproceedings{enweremConsensusBasedLeaderFollowerFormation2023a,
title = {Consensus-{{Based Leader-Follower Formation Tracking}} for {{Control-Affine Nonlinear Multiagent Systems}}},
booktitle = {2023 9th {{International Conference}} on {{Control}}, {{Decision}} and {{Information Technologies}} ({{CoDIT}})},
author = {Enwerem, Clinton and Baras, John S.},
year = {2023},
pages = {1226--1231},
issn = {2576-3555},
doi = {10.1109/CoDIT58514.2023.10284199},
url = {https://ieeexplore.ieee.org/abstract/document/10284199},
urldate = {2024-12-20},
eventtitle = {2023 9th {{International Conference}} on {{Control}}, {{Decision}} and {{Information Technologies}} ({{CoDIT}})},
keywords = {Aerospace electronics,consensus,Control systems,control-affine nonlinear systems,formation control,multiagent systems,Numerical models,Numerical simulation,Topology,Trajectory,Trajectory tracking},
}
Preprints/Reports
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.
paper 
@online{enweremDistributedOptimalFormation2023,
title = {Distributed {{Optimal Formation Control}} for an {{Uncertain Multiagent System}} in the {{Plane}}},
author = {Enwerem, Clinton and Baras, John and Romero, Danilo},
date = {2023-01-14},
eprint = {2301.05841},
eprinttype = {arXiv},
eprintclass = {cs, eess},
doi = {10.48550/arXiv.2301.05841},
url = {http://arxiv.org/abs/2301.05841},
urldate = {2023-01-18},
abstract = {In this paper, we present a distributed optimal multiagent control scheme for quadrotor formation tracking under localization errors. Our control architecture is based on a leader-follower approach, where a single leader quadrotor tracks a desired trajectory while the followers maintain their relative positions in a triangular formation. We begin by modeling the quadrotors as particles in the YZ-plane evolving under dynamics with uncertain state information. Next, by formulating the formation tracking task as an optimization problem -- with a constraint-augmented Lagrangian subject to dynamic constraints -- we solve for the control law that leads to an optimal solution in the control and trajectory error cost-minimizing sense. Results from numerical simulations show that for the planar quadrotor model considered -- with uncertainty in sensor measurements modeled as Gaussian noise -- the resulting optimal control is able to drive each agent to achieve the desired global objective: leader trajectory tracking with formation maintenance. Finally, we evaluate the performance of the control law using the tracking and formation errors of the multiagent system.},
pubstate = {prepublished},
keywords = {Computer Science - Multiagent Systems,Electrical Engineering and Systems Science - Systems and Control},
}