The CMDragons are the champions of the RoboCup Small Size League (SSL) of robot soccer. I was part of the team when we obtained second place in 2013 and 2014, and I was co-leader of the team when we won the tournament in 2015 (much credit to all the members of the team). The CMDragons won all 6 tournament games, scoring a total of 48 goals while conceding 0, and successfully completing 194 passes, with a 79% passing success rate. This level of coordinated performance is unprecedented in the RoboCup SSL.

The SSL Robots use a centralized vision and computing system to perceive and determine how to act upon their world. Since the perception and communication problems are relatively minor in this domain, our team focuses on research to enable effective and efficient coordinated decision-making in a highly dynamic adversarial environment.

We achieved our 2015 victory through various offense coordination algorithms for effective passing and positioning. My contributions include planning and execution algorithms for coordinated passing in the presence of opponents, and an an online adaptation algorithm to exploit weaknesses in the adversary's defense during free kicks.

These videos show highlights from the 2015 RoboCup tournament, and from the final, respectively.

The CoBot robots are indoor mobile service robots that autonomously perform tasks in the Gates-Hillman Center at Carnegie Mellon University. While their autonomy is very reliable under normal circumstances, my work focuses on making the CoBots more robust when abnormal situations arise. Examples of these abnormal situations include malfunctioning sensors or actuators, unsafe input or actions from humans, and uncommon events in the building. Two safety problems arise: (1) how can robots detect and describe the contexts in which these anomalies occur, and (2) how can robots make decisions that are robust to these anomalies in the presence of uncertainty.

As autonomous robots move into unsupervised, real world scenarios, they become exposed to adversarial entities or environments that may compromise the integrity of the transmitted sensor data. However robots often have multiple sensors that provide the same information (e.g., a robot's position can be obtained from wheel encoders and GPS data); this redundancy in sensors often provides enough information to detect, with a certain degree of confidence, when data coming from some of the sensors has been compromised. Using the LandShark outdoor ground vehicle as a testing platform, I work on using statistical met hods to enable mobile robots to autonomously detect when a subset of their sensors have been compromised.

Effective autonomous navigation is the most basic task that robots need to perform to be autonomous in unpredictable environments. One of my past projects consisted of extending a dynamical-systems based local path planner to allow non-holonomic mobile robots to navigate safely when faced with the particular challenges of indoor environments, such as clutter, narrow spaces and non-convex obstacles. The modified model reduced local minima problems during autonomous reactive navigation and therefore improved effectiveness of navigation. The following video is an example of our navigation work, demonstrated by a simulation; our agents can complete relatively complicated navigation tasks, even though the only information they have at any time is the relative position of their targets and of any obstacles not occluded from their perspectives.