Vision is a core component of today’s autonomous robots systems, enabling the robot to have a perception of the environment and interact with it. The main focus of this work is to create the vision pipeline for an assistive-robotics platform that will enable people with disabilities to perform activities of daily living.
The potential for invasive Brain Computer Interface (iBCI) systems to dramatically improve the livelihoods of those who have lost motor functionality has increased in recent years. This improvement has emerged due in part to advances in the field of deep learning, but the performance of these models varies significantly depending on the quality of the data collection and the design of the experiment. For iBCI participants who have lost the ability to move, the decoding models will be attempting to decode imagined movement, rather than actual movement. To have the best chances at decoding imagined movement, an effective and realistic virtual environment is essential as it increases the chance that the iBCI participant effectively embodies the virtual arm, thereby allowing for synchronization between the collected neural data and the imagined arm position. This position allows the researcher to work alongside experts in neuroscience, machine learning, and robotics in a supportive and resource-rich environment.