3 accepted papers
The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative particularly fo…
Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete (e.g., binary) object states, which poses challenges for learning complex tasks and transferring learned policy from the simul…
We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actio…