I develop robot learning methods for
contact-rich dexterous manipulation under
uncertainty. My work focuses on learned policies that use
vision, proprioception, and tactile feedback to grasp, stow, and manipulate objects when
object pose, friction, contact modes, and target geometry are only partially observed.
I am especially interested in risk-aware policy execution,
and study how robots can estimate uncertainty from multiple sources (proprioceptive, perceptual, and tactile)
during learned manipulation, recognize when a policy is entering a likely failure mode, and decide when to recover,
replan, or gather more information. My work connects
visuo-tactile policy learning,
belief estimation,
distributional reinforcement learning, and
risk-sensitive control to build more robust systems for physical autonomy.
I validate these ideas in MuJoCo, Isaac Sim, reinforcement-learning environments, and on
real robot platforms equipped with anthropomorphic or tactile-sensorized dexterous hands.
Applications
I apply the aforementioned ideas to robot learning systems for:
Visuo-tactile dexterous grasping under uncertain object pose, friction, and contact modes
Risk-aware execution for learned manipulation policies that anticipate and recover from likely failures
Learning from uncertainty-aware expert demonstrations for contact-rich manipulation tasks
Safe robot navigation and control using distributional reinforcement learning and risk-sensitive planning
Robust Dexterous Grasping
Learned Manipulation Policy
Safe Visuo-Tactile Manipulation
Risk-Aware Robot Learning
PublicationsPreprints (Submitted/Under Review)
2026
arXiV
Clinton Enwerem, Shreya Kalyanaraman, John S. Baras, and Calin Belta, “Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty,” arXiv preprint, 2026. Extended preprint with additional experiments, analysis, and discussion. arXiv link
@online{enweremVariationalNeuralBeliefParameterizations2026,
title = {Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty},
author = {Enwerem, Clinton and Kalyanaraman, Shreya and Baras, John S. and Belta, Calin},
year = {2026},
eprint = {2604.25897},
eprinttype = {arXiv},
eprintclass = {cs.RO},
doi = {10.48550/arXiv.2604.25897},
url = {http://arxiv.org/abs/2604.25897},
urldate = {2026-04-28},
pubstate = {prepublished},
keywords = {Computer Science - Robotics, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control},
}
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
@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.
@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.
@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.
@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.
@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.
@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.
@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},
}
