Bio-inspired robots; medical robots; collaborative, cooperative and networked robots; robotics for extreme environments; unmanned air and water vehicles; AI and computer vision for robots

ISR faculty have advanced flocking and swarming theory, control and design for robotic groups and swarms. ISR developed motion description languages; designed and fabricated a modular dexterous hand; made advances in underwater robots, flapping-wing micro air vehicles and micro robots; and introduced innovations in computer vision and hyperdimensional computing theory for robots. A multi-part, in-mold assembly process that reduces process cost and enables robotic design possibilities created articulated structures without requiring post-molding assembly steps. ISR started the Maryland Robotics Center as one of its initiatives in 2010.

Recent ISR robotics publications


Robotic Parasitic Array Optimization in Outdoor Environments

Jeffrey Twigg, Fikadu Dagefu, Nikhil Chopra, Brian M. Sadler

The paper describes a two-element parasitic array formed by two ground robots and proposes a technique by which this array can optimize its gain in a direction of interest online.

2019 IEEE International Symposium on Safety, Security, and Rescue Robotics


Experimental Comparison of Decentralized Task Allocation Algorithms Under Imperfect Communication

Sharan Nayak, Mohamed Khalid M. Jaffar, Estefany Carrillo, Suyash Yeotikar, Eliot Rudnick-Cohen, Ruchir Patel, Jeffrey Herrmann, Huan Xu, Shapour Azarm, Michael Otte

An experimental comparison of the performance of five state-of-the-art decentralized task allocation algorithms under imperfect communication conditions for teams of unmanned aerial vehicles (UAVs).

IEEE Robotics and Automation Letters

Data-driven Metareasoning for Collaborative Autonomous Systems

Jeffrey Herrmann

A novel data-driven metareasoning approach that generates a metareasoning policy that agents in a multi-agent system can use whenever they must collaborate to assign tasks.









ISR Technical Report, DRUM


Deep Differentiable Grasp Planner for High-DOF Grippers

Min Liu, Zherong Pan, Kai Xu, Kanishka Ganguly, Dinesh Manocha

An end-to-end algorithm for training deep neural networks to grasp novel objects.

Grasping Fragile Objects using a Stress-Minimization Metric

Zherong Pan, Xifeng Gao, Dinesh Manocha

A new stress-minimization metric to generate optimal grasps for brittle and fragile objects.


Reinforcement Learning-Based Visual Navigation with Information-Theoretic Regularization

Qiaoyun Wu, Kai Xu, Jun Wang, Mingliang Xu, Dinesh Manocha

The authors integrate an information-theoretic regularization into a deep reinforcement learning framework for the target-driven task of visual navigation in robotics. This is achieved by first learning to generate a next observation from a current observation and a navigation target, then planning an action toward the target based on the generated observation and the current observation.

New Formulation of Mixed-Integer Conic Programming for Globally Optimal Grasp Planning

Min Liu, Zherong Pan, Kai Xu, and Dinesh Manocha

Classical robotics research presents the first grasp planning algorithm to compute globally optimal gripper pose that maximizes a grasp metric.

DCAD: Decentralized Collision Avoidance with Dynamics Constraints for Agile Quadrotor Swarms

Senthil Hariharan Arul, Dinesh Manocha

A new algorithm for decentralized collision avoidance for quadrotor swarm navigation in dense environments with static and dynamic obstacles.

3-D Deformable Object Manipulation using Deep Neural Networks

Zhe Hu, Tao Han, Peigen Sun, Jia Pan, Dinesh Manocha

Deep neural network-based controller to servo-control position and shape of deformable objects with unknown deformation properties.

IEEE Robotics and Automation Letters

DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance

Qingyang Tan, Tingxiang Fan, Jia Pan, Dinesh Manocha

A novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning. | Watch a video about DeepMNavigate |

GraphRQI: Classifying Driver Behaviors Using Graph Spectrums

Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Xiaoyu Li, Aniket Bera, Dinesh Manocha

The GraphRQI algorithm identifies driver behaviors from road agent trajectories. It is 25 percent more accurate over prior behavior classification algorithms for autonomous vehicles.  | Watch a video about GraphRQI |

Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding

Qingyang Tan, Zherong Pan, Lin Gao, and Dinesh Manocha

A new method bridges the gap between mesh embedding and physical simulation for efficient dynamic models of clothes. The key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies, and a new mesh embedding approach based on physics-inspired loss term. After training, the learned simulator runs10–100 times faster and the accuracy is high enough for robot manipulation tasks. | Watch a video about this method |


Bioinspired pursuit with a swimming robot using feedback control of an internal rotor

Brian Free, Jinseong Lee, Derek Paley

The paper presents a state-feedback control law for steering a fish-inspired robot in a desired direction, and engaging it in pure pursuit.

Bioinspiration and Biomimetics

Cooperative Mapping and Target Search over an Unknown Occupancy Graph Using Mutual Information

Artur Wolek, Sheng Cheng, Debdipta Goswami, Derek Paley

A cooperative mapping and target-search algorithm for a team of autonomous quadrotors equipped with noisy, range-limited sensors. The algorithm can concurrently map and search an unknown urban area, while detecting and tracking a mobile ground target.

IEEE Robotics and Automation Letters

Feedback Control of a Soft Swinging Appendage

Travis Burch, John Lathrop, William Scott, Derek Paley

A state-space description using planar discrete elastic rod theory of a soft robotic appendage with torque input at one end. The authors design a linear output feedback controller to balance the appendage in an unstable vertical configuration with an angle sensor and torque input co-located at the base.

CDCL paper

Geometric Attitude and Position Control of a Quadrotor in Wind

William Craig, Derrick Yeo, Derek Paley

The researchers use  a model of the aerodynamic interaction between the propellers and wind, paired with onboard flow sensing and feedback control, to improve the stability of quadrotors in unsteady winds with the long-term goal of enabling reliable outdoor flight in windy conditions.

AIAA Journal of Guidance, Control, and Dynamics

Orbit Design for Cislunar Space Domain Awareness

Erin Fowler, Stella Hurtt, Derek Paley

Quantitative assessments of the orbits and sensor characteristics of satellites intended for cislunar space domain awareness.

2nd IAA Conference on Space Situational Awareness



Output Feedback Control for Lift Maximization of a Pitching Airfoil

Justin M. Lidard, Debdipta Goswami, David Snyder, Girguis Sedky, Anya Jones, Derek Paley

Unsteady aerodynamics is driving research at the interface of fluid dynamics and control theory for low Reynolds number aircraft such as micro air vehicles (MAVs). The regulation and control of unsteady behavior is crucial for maintaining the stability of an MAV, which necessitates accurate modeling of their flight surfaces. This paper describes the implementation of the Goman-Khrabrov model for flow stagnation near an actuated airfoil with a feedback-controlled pitch rate for the purpose of maximizing the time-averaged unsteady lift.

AIAA SciTech 2020 Forum

Non-Gaussian Estimation of a Potential Flow by an Actuated Lagrangian Sensor Steered to Separating Boundaries by Augmented Observability

Francis D. Lagor, Kayo Ide, Derek Paley

An architecture for estimation of a flow field using a hypothetical oceanographic vehicle that is guided along paths of high flow-field observability, a concept quantifying the informativeness of a path.

IEEE Journal of Oceanic Engineering

Mobile Sensor Networks Control: Adaptive Sampling of Spatiotemporal Processes

Derek Paley, Artur Wolek

A review of control of mobile sensor networks for environmental monitoring and other applications. Includes sensor platform dynamics and cooperative control and estimation; process modeling and estimation for both continuous and discrete models; sampling metrics and optimization, including coverage-, topology-, information-, and estimation-based metrics; and methods for task design and allocation.

Annual Review of Control, Robotics, and Autonomous Systems

Stabilization of a Quadrotor in Wind with Flow Sensing: Linear Modeling and Control for Attitude and Position Hold

William Craig, J. T. Lewis, Derek Paley

This paper describes a linear controller that has been optimized for gust rejection using CONDUITR©, based on system identification performed with CIFERR©. Additionally, a custom flow probe package was used to investigate the benefits of flow feedback for gust rejection. Experiments were performed with a 210mm quadrotor system running Cleanflight software, where flow feedback yielded improvements for both short and long time-scale gusts, particularly for the longer time-scale five second gusts when the vehicle had time to settle in the wind.

VFS Autonomous VTOL Technical Meeting and Electric VTOL Symposium



Recognizing Hemiparetic Ankle Deficits Using Wearable Pressure Sensors

Ahmed Ramadan, Anindo Roy, Elisabeth Smela

This paper provides proof of concept for a novel method to recognize impaired push-off and foot-drop deficits in hemiparetic gait using analog pressure sensors. These data may enhance feedback from a modular ankle exoskeleton (such as Anklebot) for stroke rehabilitation.

IEEE Journal of Translational Engineering in Health and Medicine


Experimental Comparison of Decentralized Task Allocation Algorithms Under Imperfect Communication

Sharan Nayak, Mohamed Khalid M. Jaffar, Estefany Carrillo, Suyash Yeotikar, Eliot Rudnick-Cohen, Ruchir Patel, Jeffrey Herrmann, Huan Xu, Shapour Azarm, Michael Otte

An experimental comparison of the performance of five state-of-the-art decentralized task allocation algorithms under imperfect communication conditions for teams of unmanned aerial vehicles (UAVs).

IEEE Robotics and Automation Letters

About the Maryland Robotics Center

Learn about the Maryland Robotics Center's research and education opportunities in this video.

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ISR and Maryland Robotics Center robotics news