6 accepted papers
In the field of robotics, event-based cameras are emerging as a promising low-power alternative to traditional frame-based cameras for capturing high-speed motion and high dynamic range scenes. This is due to their sparse and asynchronous event outputs. Spiking Neural Networks (SNNs) with their asyn…
Contact sensing and localization capabilities that mimic human skin are highly desirable for robots. In this paper, we introduce AcouSkin, an acoustic wave based full surface contact localization system. Acoustic waves produced by piezoelectric transceivers using a monotone are coupled to surfaces t…
Event-based cameras have recently shown great potential for high-speed motion estimation owing to their ability to capture temporally rich information asynchronously. Spiking Neural Networks (SNNs), with their neuro-inspired event-driven processing can efficiently handle such asynchronous data, whil…
Standard frame-based cameras that sample light intensity frames are heavily impacted by motion blur for high-speed motion and fail to perceive scene accurately in high-dynamic range environments. Event-based cameras, on the other hand, overcome these limitations by asynchronously detecting the varia…
Present-day Deep Reinforcement Learning (RL) systems show great promise towards building intelligent agents surpassing human-level performance. However, the computational complexity associated with the underlying deep neural networks (DNNs) leads to power-hungry implementations. This makes deep RL s…
Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based cameras suffer critically. This is attributed to their high temporal resolution, high dynamic range, and low-power consumptio…