My research is focused on the development of active runtime learning techniques for robotic systems.
Ultimately, my goal is to enhance robotic systems to be selfsufficient and adaptive with minimal
human intervention.


Hybrid Control for Learning Motor Skills
Ian Abraham, Alexander Broad, Allison Pinosky, Brenna Argall, Todd Murphey
Workshop on Algorithmic Foundations of Robotics , 2020
We develop a hybrid control approach for robot learning based on combining learned predictive models with
experiencebased stateaction policy mappings to improve the learning capabilities of robotic systems. Hybrid control
tools are used to create an algorithmic approach for combining learned predictive models with experiencebased learning.
Hybrid learning is presented as a method for efficiently learning motor skills by systematically combining and improving
the performance of predictive models and experience based policies.


An Ergodic Measure for Active Learning From Equilibrium
Ian Abraham, Ahalya Prabhakar, Todd Murphey
IEEE Transactions on Automation Science and Engineering, 2020
This paper develops KLErgodic Exploration from Equilibrium (KLE 3 ), a method for robotic systems to integrate
stability into actively generating informative measurements through ergodic exploration. Ergodic exploration enables
robotic systems to indirectly sample from informative spatial distributions globally, avoiding local optima, and
without the need to evaluate the derivatives of the distribution against the robot dynamics.


Modelbased Generalization under Parameter Uncertainty using Path Integral Control
Ian Abraham, Ankur Handa, Nathan Ratliff, Kendall Lowrey, Todd Murphey, Dieter Fox
IEEE Robotics and Automation Letters, 2020
project page
This work addresses the problem of robot interaction in complex environments where online control and adaptation is
necessary. By expanding the sample space in the free energy formulation of path integral control, we derive a natural
extension to the path integral control that embeds uncertainty into action and provides robustness for modelbased robot
planning.


Active Area Coverage from Equilibrium
Ian Abraham, Ahalya Prabhakar, ToddD Murphey
Workshop on Algorithmic Foundations of Robotics, 2019
This paper develops a method for robots to integrate stability into actively seeking out informative measurements
through coverage. We show that our method is able to maintain Lyapunov attractiveness while still actively seeking out
data.


WHISKiT Physics: A threedimensional mechanical model of the rat vibrissal array
NadinaO Zweifel, NickE Bush, Ian Abraham, ToddD Murphey, MitraJZ Hartmann
bioRxiv, 2019
Rodents tactually explore the environment using ~62 whiskers (vibrissae), regularly arranged in arrays on both sides of
the face. The rat vibrissal system is one of the most commonly used models to study how the brain encodes and processes
somatosensory information. To date, however, researchers have been unable to quantify the mechanosensory input at the
base of each whisker, because the field lacks accurate models of threedimensional whisker dynamics. To close this gap,
we developed WHISKiT Physics, a simulation framework that incorporates realistic morphology of the full rat whisker
array to predict timevarying mechanical signals for all whiskers.


Datadriven Koopman Operators for Modelbased Shared Control of HumanMachine Systems
Alexander Broad, Ian Abraham, Brenna Argall, Todd Murphey
(conditionally Accepted) International Journal of Robotics Research, 2019
We present a datadriven shared control algorithm that can be used to improve a human operator’s control of complex
dynamic machines and achieve tasks that would otherwise be challenging, or impossible, for the user on their own. Our
method assumes no a priori knowledge of the system dynamics. Instead, both the dynamics and information about the user’s
interaction are learned from observation through the use of a Koopman operator.


Active Learning of Dynamics for DataDriven Control using Koopman Operators
Ian Abraham, Todd D. Murphey
IEEE Transactions on Robotics (KingSun Fu Best Paper Award), 2019
project page
code
This paper presents an active learning strategy for robotic systems that takes into account task information, enables
fast learning, and allows control to be readily synthesized by taking advantage of the Koopman operator representation.


Decentralized ergodic control: distributiondriven sensing and exploration for multiagent systems
Ian Abraham, Todd D. Murphey
IEEE Robotics and Automation Letters, 2018
We present a decentralized ergodic control policy for timevarying area coverage problems for multiple agents with
nonlinear dynamics. Ergodic control allows us to specify distributions as objectives for area coverage problems for
nonlinear robotic systems as a closedform controller.


DataDriven Measurement Models for Active Localization in Sparse Environments
Ian Abraham, Anastasia Mavrommati, ToddD Murphey
Robotics: Science and Systems, 2018
We develop an algorithm to explore an environment to generate a measurement model for use in future localization tasks.
Ergodic exploration with respect to the likelihood of a particular class of measurement (eg, a contact detection
measurement in tactile sensing) enables construction of the measurement model. Exploration with respect to the
information density based on the datadriven measurement model enables localization.


Structured neural network dynamics for modelbased control
Alexander Broad, Ian Abraham, Todd Murphey, Brenna Argall
Learning and Inference in Robotics  Robotics: Science and Systems, 2018
We present a structured neural network architecture that is inspired by linear timevarying dynamical systems. The
network is designed to mimic the properties of linear dynamical systems which makes analysis and control simple. The
architecture facilitates the integration of learned system models with gradientbased model predictive control
algorithms, and removes the requirement of computing potentially costly derivatives online.


RealTime Area Coverage and Target Localization using RecedingHorizon Ergodic Exploration
Anastasia Mavrommati, Emmanouil Tzorakoleftherakis, Ian Abraham and Todd D. Murphey
IEEE Transactions on Robotics, 2017
code
Although a number of solutions exist for the problems of coverage, search and target localization—commonly addressed
separately—whether there exists a unified strategy that addresses these objectives in a coherent manner without being
applicationspecific remains a largely open research question. In this paper, we develop a recedinghorizon ergodic
control approach, based on hybrid systems theory, that has the potential to fill this gap.


Ergodic exploration using binary sensing for nonparametric shape estimation
Ian Abraham, Ahalya Prabhakar, MitraJZ Hartmann, ToddD Murphey
IEEE Robotics and Automation Letters, 2017
Current methods to estimate object shape—using either vision or touch—generally depend on highresolution sensing. Here,
we exploit ergodic exploration to demonstrate successful shape estimation when using a lowresolution binary contact
sensor.


ModelBased Control Using Koopman Operators
Ian Abraham, Gerardo De La Torre, Todd D. Murphey
Robotics: Science and Systems, 2017
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This paper explores the application of Koopman operator theory to the control of robotic systems. The Koopman operator
takes any observation of state at time t and evolves the functions of state subject to its dynamics forward in time
linearly. Thus, a nonlinear dynamical system which has a discoverable Koopman operator can be represented by a linear
Koopman operator in a lifted function space where the observations of state evolve linearly. We illustrate how the
operator can be used to obtain a linearizable datadriven model of robotic systems for modelbased control synthesis.


A nonlinear leg damping model for the prediction of running forces and stability
Ian Abraham, ZhuoHua Shen, Justin Seipel
Journal of Computational and Nonlinear Dynamics, 2015
Despite the neuromechanical complexity underlying animal locomotion, the steadystate centerofmass motions and ground
reaction forces of animal running can be predicted by simple springmass models such as the canonical springloaded
inverted pendulum (SLIP) model. Here, we develop a leg damping model that is bilinear in leg length and velocity in
order to yield improved damping force and ground reaction force prediction.


Model predictive control of buoyancy propelled autonomous underwater glider
Ian Abraham, Jingang Yi
ACC American Control Conference, 2015
Autonomous underwater gliders (AUG) is a costeffective and efficient tool for oceanic exploration and discovery. In
this paper, we present a model predictive control (MPC) design to compensate for the drift due to disturbances.

