Unlocking Generalization for Robotics via Modularity and Scale
Abstract: How can we build generalist robot systems? Looking at fields such as vision and language, the common theme has been large scale end-to-end learning with massive, curated datasets. In robotics, on the other hand, scale alone may not be enough due to the significant multimodality of robotics tasks, lack of easily accessible data and [...]
Leveraging Affordances for Accelerating Online RL
Abstract: The inability to explore environments efficiently makes online RL sample-inefficient. Most existing works tackle this problem in a setting devoid of prior information. However, additional affordances may often be cheaply available at the time of training. These affordances include small quantities of demo data, simulators that can reset to arbitrary states and domain specific [...]
Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns
Abstract: Roadway congestion leads to wasted time and money and environmental damage. One possible solution is adding more roadway capacity, but this can be impractical especially in urban environments and still may not make up for a poorly-calibrated traffic signal schedule. As such, it is becoming increasingly important to use existing road networks more efficiently. [...]