Juan Pablo Mendoza

Focused Optimization for Online Detection of Anomalous Regions

This paper presents an online algorithm for early detection of anomalies in robot execution, where the anomalies occur in a particular region of the robots state space. Assuming that a model of normal execution is given, the algorithm detects regions of space where data significantly deviate from normal. It achieves this by focusing optimization over a fixed-parameter family of shapes to find the one among them that is most likely anomalous, and then using this region to decide whether execution is anomalous. Experiments using synthetic and real robot data support the effectiveness of the approach.

Motion interference detection in mobile robots

As mobile robots become better equipped to autonomously navigate in human-populated environments, they need to become able to recognize internal and external factors that may interfere with successful motion execution. Even when these robots are equipped with appropriate obstacle avoidance algorithms, collisions and other forms of motion interference might be inevitable: there may be obstacles in the environment that are invisible to the robots sensors, or there may be people who could interfere with the robots motion. We present a Hidden Markov Model-based model for detecting such events in mobile robots that do not include special sensors for specific motion interference. We identify the robot observable sensory data and model the states of the robot. Our algorithm is motivated and implemented on an omnidirectional mobile service robot equipped with a depth-camera. Our experiments show that our algorithm can detect over 90% of motion interference events while avoiding false positive detections.

CMDragons 2015: Coordinated Offense and Defense of the SSL Champions

The CMDragons Small Size League SSL team won all of itsi¾ź6 games at RoboCup 2015, scoring a total of 48 goals and conceding 0. This paper presents the core coordination algorithms in offense and defense that enabled such successful performance. We first describe the coordinated plays layer that distributes the teams robots into offensive and defensive subteams. We then describe the offense and defense coordination algorithms to control these subteams. Effective coordination enables our robots to attain a remarkable level of team-oriented gameplay, persistent offense, and reliability during regular gameplay, shifting our strategy away from stopped ball plays. We support these statements and the effectiveness of our algorithms with statistics from our performance at RoboCup 2015.

High-Assurance SPIRAL: End-to-End Guarantees for Robot and Car Control

Cyberphysical systems (CPSs), ranging from critical infrastructures such as power plants, to modern (semi) autonomous vehicles, are systems that use software to control physical processes. CPSs are made up of many different computational components. Each component runs its own piece of software that implements its control algorithms, based on its model of the environment. Every component then interacts with other components through the signals and values it sends out. Collectively, these components, and the code they run, drive the complex behaviors modern society has come to expect and rely on. Due to these intricate interactions between components, managing the hundreds to millions of lines of software to ensure that the system, as a whole, performs as desired can often be unwieldy.

Plan execution monitoring through detection of unmet expectations about action outcomes

Modeling the effects of actions based on the state of the world enables robots to make intelligent decisions in different situations. However, it is often infeasible to have globally accurate models. Task performance is often hindered by discrepancies between models and the real world, since the true outcome of executing a plan may be significantly worse than the expected outcome used during planning. Furthermore, expectations about the world are often stochastic in robotics, making the discovery of model-world discrepancies non-trivial. We present an execution monitoring framework capable of finding statistically significant discrepancies, determining the situations in which they occur, and making simple corrections to the world model to improve performance. In our approach, plans are initially based on a model of the world that is only as faithful as computational and algorithmic limitations allow. Through experience, the monitor discovers previously unmodeled modes of the world, defined as regions of a feature space in which the experienced outcome of a plan deviates significantly from the predicted outcome. The monitor may then make suggestions to change the model to match the real world more accurately. We demonstrate this approach on the adversarial domain of robot soccer: we monitor pass interception performance of potentially unknown opponents to try to find unforeseen modes of behavior that affect their interception performance.

Opponent-Aware Ball-Manipulation Skills for an Autonomous Soccer Robot

Autonomous robot soccer requires effective multi-agent planning and execution, which ultimately relies on successful skill execution of individual team members. This paper addresses the problem of ball-manipulation for an individual robot already in possession of the ball. Given a planned pass or shoot objective, the robot must intelligently move the ball to its target destination, while keeping it away from opponents. We present and compare complementary ball-manipulation skills that are part of our CMDragons team, champion of the 2015 RoboCup Small Size League. We also present an approach for selecting the appropriate skill given the state of the world. To support the efficacy of the approach, we first show its impact in real games through statistics from the RoboCup tournament. For further evaluation, we experimentally demonstrate the advantages of each introduced skill in different sub-domains of robot soccer.

Skills, Tactics and Plays for Distributed Multi-robot Control in Adversarial Environments

This work presents a pioneering collaboration between two robot soccer teams from different RoboCup leagues, the Small Size League (SSL) and the Middle Size League (MSL). In the SSL, research is focused on fast-paced and advanced team play for a centrally-controlled multi-robot team. MSL, on the other hand, focuses on controlling a distributed multi-robot team. The goal of cooperation between these two leagues is to apply teamwork techniques from the SSL, which have been researched and improved for years, in the MSL. In particular, the Skills Tactics and Plays (STP) team coordination architecture, developed for centralized multi-robot team, is studied and integrated into the distributed team in order to improve the level of team play. The STP architecture enables more sophisticated team play in the MSL team by providing a framework for team strategy adaptation as a function of the state of the game. Voting-based approaches are proposed to overcome the challenge of adapting the STP architecture to a distributed system. Empirical evaluation of STP in the MSL team shows a significant improvement in offensive game play when distinguishing several offensive game states and applying appropriate offensive plays.