Interactive & Emergent Autonomy Lab


Active Learning and Data-Driven Control Active Perception in Human-Swarm Collaboration Algorithmic Matter and Emergent Computation Control for Nonlinear and Hybrid Systems Cyber Physical Systems in Uncertain Environments Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices

Haptic Languages

Shape Learning

Shape estimation using binary sensing and ergodic exploration

We are interested in how robots can construct symbolic representations from mechanical contact. For instance, how can a robot feel an object with fingertip sensors and generate a symbolic representation of that object that is rich enough to identify the object in the future? How the robot runs the fingertip over the object will greatly influence the representation it gets, so determining control laws from the learning need is a major part of this research. In the image above, we focused on enabling a robot to determine the shape of an object by interacting with it and measuring only its end effector kinematics. Over a short amount of time, the shape stabilizes to a fixed representation that can then be used in the future for searching for the object. Goals of this work include the following.


Mitra Hartmann, Northwestern University


Real-time area coverage and target localization using receding-horizon ergodic exploration
A. Mavrommati, E. Tzorakoleftherakis, I. Abraham, and T. D. Murphey
IEEE Transactions on Robotics, vol. 34, no. 1, pp. 62–80, 2018. PDF, Video 1, Video 2

Data-driven measurement models for active localization in sparse environments
I. Abraham, A. Mavrommati, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2018. PDF, Video

Ergodic exploration using binary sensing for non-parametric shape estimation
I. Abraham, A. Prabhakar, M. Hartmann, and T. Murphey
IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 827–834, 2017. PDF, Video

Autonomous visual rendering using physical motion
A. Prabhakar, A. Mavrommati, J. Schultz, and T. D. Murphey
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2016. PDF, Video 1, Video 2, Video 3


This project is funded by the National Science Foundation–National Robotics Initiative: Autonomous Synthesis of Haptic Languages.