Research

overview & theory

I develop optimization-based motion planning and control algorithms for safe and robust physical autonomy in uncertain, dynamic, and partially observed environments. My work integrates stochastic optimization, risk-sensitive distributional reinforcement learning (DRL), and safety-critical control through methods such as quadratic optimization, model predictive path integral control (MPPI), and stochastic model predictive control (MPC) to enable principled decision-making for embodied, intelligent, and autonomous (single- and multi-robot) systems. I focus on uncertainty-aware decision-making with explicit performance and safety objectives, designing algorithms that provide task-level performance guarantees while maintaining robustness and computational tractability in real-world settings. I validate these methods in high-fidelity simulation (MuJoCo, Isaac Sim), reinforcement learning environments (OpenAI Gym, Safety Gymnasium), and on real robotic platforms, including serial-chain manipulators equipped with high-DoF anthropomorphic end-effectors.

Applications

Risk-sensitive planning and control undergird systems I have built for:

  • Robust multi-finger grasp generation under variable object pose and uncertain friction
  • Collision-free dexterous manipulation using robot arms fitted with anthropomorphic end-effectors
  • Imitation learning from uncertainty-aware expert trajectories in stochastic environments
  • Safe wheeled robot navigation in obstacle-rich environments

GraspIt Demo

Robust Grasp Synthesis

GraspIt Demo

Learned Policy Rollout

Safety-Critical Control Demo

Safe Manipulator Control

Demonstration of Risk-Aware QR-DQN in action

Robot Navigation

Publications

Under Review
2026

arXiV
Clinton Enwerem, John S. Baras, and Calin Belta, “Risk-Constrained Belief-Space Optimization for Safe Control under Latent Uncertainty,” arXiv preprint, 2026. arXiv link
paper 
@online{enweremRiskConstrainedBeliefOptimization2026,
title = {Risk-Constrained Belief-Space Optimization for Safe Control under Latent Uncertainty},
author = {Enwerem, Clinton and Baras, John S. and Belta, Calin},
year = {2026},
eprint = {2604.03868},
eprinttype = {arXiv},
eprintclass = {cs.RO},
doi = {10.48550/arXiv.2604.03868},
url = {http://arxiv.org/abs/2604.03868},
urldate = {2026-04-07},
pubstate = {prepublished},
keywords = {Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control},
}
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. 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},
}