Pawels' research interests are in the field of low power artificial intelligent systems. His work ranges from running these systems in simulation (CoppeliaSim, Mujoco, Airsim, Pygame) to real robotics (robotic arms and drones). The low power abilities are directly due to the neuromorphic modelling approach, first outlined in the Neural Engineering Framework. The algorithms Pawel focus' on are for adaptive motor control and visual navigation. The aim for his thesis is to develop a low power visual navigation system, through the coupling of Legendre Memory Units and feature extraction, capable of multi-scale temporal processing.

Publications

Journal Articles

• Travis DeWolf, Kinjal Patel, Pawel Jaworski, Roxana Leontie, Joseph Hays, Chris Eliasmith (2023) Neuromorphic control of a simulated 7-DOF arm using Loihi. Neuromorphic Computing and Engineering.  Abstract  External link
• Travis DeWolf, Pawel Jaworski, Chris Eliasmith (2020) Nengo and Low-Power AI Hardware for Robust Embedded Neurorobotics. Frontiers in Neurorobotics.  Abstract  External link

Conference and Workshop Papers

• Brent Komer, Pawel Jaworski, Steven Harbour, Chris Eliasmith, Travis DeWolf (2022) BatSLAM: Neuromorphic Spatial Reasoning in 3D Environments. In 41st Digital Avionics Systems Conference. Portsmouth, VA, USA. DASC.  Abstract  PDF