Welcome to the Autonomous Systems Laboratory
The autonomous systems lab (ASL) develops algorithms and control methods for autonomous systems operating in uncertain environments. Systems include autonomous ground and aerial vehicles, multi-robot systems, autonomous driving, and robots that interact with humans.
The following are some current areas of focus:
- Robot motion planning: methods for planning robot motion to efficiently complete complex tasks.
- Improving robot performance over time: learning robot behaviors and policies through repeated task executions.
- Planning under uncertainty: planning and sensing for operation in unknown environments.
- Persistent monitoring and scene reconstruction: monitoring and building real-time maps for complex 3D environments.
- Future transportation systems: coordinating and dispatching vehicles for ride-sharing and urban transportation.
- Human-robot interaction: coordinating robots to work with humans in task specification and collaborative assembly.
- Distributed and submodular optimization: Collective decision making strategies for objectives that exhibit diminishing returns.
- Dynamic vehicle routing: efficient dispatching robots to respond to task requests in real-time.
- Other areas of interest include informative path planning, task allocation, formation control, consensus/rendezvous and ocean sampling.
- Apr. 29, 2020
Our results on Improving user specifications for robot behavior through active preference learning: Framework and evaluation have appeared in the International Journal of Robotics Research (IJRR). The preprint is available on arXiv.
- Mar. 4, 2020
Florence Tsang has completed her Master's degree! Her thesis is on Learning a Motion Policy to Navigate Environments with Structured Uncertainty.
- Dec. 17, 2019
Ryan De Iaco completed his Master's degree in Electrical & Computer Engineering! He will be starting at Waymo in the new year. Congratulations!