Clinton Enwerem Doctoral Candidate in ECE | Robot Learning for Dexterous Manipulation under Multimodal Uncertainty

I am an Electrical & Computer Engineering (ECE) Ph.D. candidate at the University of Maryland (UMD), College Park, MD, USA, working with Professor John S. Baras, Distinguished University Professor and Endowed Lockheed Martin Chair in Systems Engineering, and Professor Calin Belta, Brendan Iribe Endowed Professor of ECE and Computer Science. I work on contact-rich dexterous manipulation under uncertainty, drawing on risk-sensitive control, belief-space planning, and policy learning to make multi-fingered robot hands more reliable when object pose, friction, and contact modes are only partially known. At UMD, I am affiliated with the Institute for Systems Research, the EXplainable and Assured Control for AuTonomy (EXACT) lab, the Systems Engineering and Integration lab, and the Maryland Robotics Center (MRC). I have also received the Dean's Fellowship from UMD's Graduate School and the Microsoft Diversity in Robotics and Autonomy PhD Fellowship, awarded through a collaboration between Microsoft Corporation and MRC.

Prior to resuming doctoral studies at UMD, I was a robotics engineer at Kognitive Robotics, a local robotics engineering startup building turnkey mobile robot platforms for education and research. Before that, I completed a year-long stint as a robotics trainee at Nigeria's foremost robotics and AI research center — Robotics and Artificial Intelligence Nigeria (RAIN). At RAIN, I worked with Dr. Olusola Ayoola on varied projects spanning robot navigation, visual SLAM, and robot control. Before RAIN, in affiliation with the Electrical Engineering Department at my alma mater, I collaborated with Ihechiluru Okoro on research topics at the intersection of robust control, observer-based compensator design, and feedback control of time-delayed dynamical systems.

I earned my undergraduate degree in Electrical Engineering (with highest honors) from the University of Nigeria, working under the supervision of Dr. Udoka Nwaneto. My bachelor's thesis focused on model-based controller design for speed regulation in electric drives.

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Contact Info

2239 A.V. Williams Bldg.

8223 Paint Branch Dr

College Park, MD 20740

(research): enwerem [at] umd [dot] edu

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Research

I study contact-rich dexterous manipulation under uncertainty, connecting risk-sensitive control and planning, belief estimation, and policy learning to build manipulation systems that handle uncertain object pose, friction, contact modes, and target geometry. I validate these ideas in MuJoCo, Drake, and on real robot platforms.

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MuJoCo belief-space grasping demo

Robust Dexterous Grasping

EquiDexFlow demo — SE(3)-equivariant reach-aware grasp synthesis

SE(3)-Equivariant, Reach-Aware Grasp Synthesis

Policy learning demo

Policy Learning from Multimodal Data

Safe manipulator control demo

Safe Data Collection & Policy Rollout

Publications and more →
News

[model] If you work on learning-based dexterous manipulation, check out EquiDexFlow, our SE(3)-equivariant flow-matching model for synthesizing contact-aware multi-fingered grasps.
[preprint] New work on variational neural belief representations that encode dexterous-grasping uncertainty from multiple sensing modalities, enabling gradient-based, uncertainty-aware grasp-quality maximization beyond what standard techniques allow. A preprint is available on arXiv.
[preprint] New work coalescing risk-constrained optimization with belief-space model predictive path integral (MPPI) control applied to vision-guided dexterous object stowing under uncertain target region geometry. A preprint is available on arXiv.
[paper] Our paper on quantile-based distributional RL for safe control has been accepted for presentation at CDC 2025.
[epoch] I passed my research proposal exam and advanced to doctoral candidacy.