I am a Professor of Computer Science at the University of Minnesota. My research is on distributed decision making for autonomous agents. I work in different application areas, ranging from distributed methods for allocation of tasks, to methods to explore an unknown environment, teamwork for search and rescue, and navigation in dense crowds. Address: Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, United States
Proceedings of the AAAI Conference on Artificial Intelligence
We propose an auction algorithm to allocate tasks that have temporal constraints to cooperative r... more We propose an auction algorithm to allocate tasks that have temporal constraints to cooperative robots. Temporal constraints are expressed as time windows, within which a task must be executed. There are no restrictions on the time windows, which are allowed to overlap. Robots model their temporal constraints using a simple temporal network, enabling them to maintain consistent schedules. When bidding on a task, a robot takes into account its own current commitments and an optimization objective, which is to minimize the time of completion of the last task alone or in combination with minimizing the distance traveled. The algorithm works both when all the tasks are known upfront and when tasks arrive dynamically. We show the performance of the algorithm in simulation with different numbers of tasks and robots, and compare it with a baseline greedy algorithm and a state-of-the-art auction algorithm. Our algorithm is computationally frugal and consistently allocates more tasks than th...
Proceedings of the AAAI Conference on Artificial Intelligence
In crowded multi-agent navigation environments, the motion of the agents is significantly constra... more In crowded multi-agent navigation environments, the motion of the agents is significantly constrained by the motion of the nearby agents. This makes planning paths very difficult and leads to inefficient global motion. To address this problem, we propose a new distributed approach to coordinate the motions of agents in crowded environments. With our approach, agents take into account the velocities and goals of their neighbors and optimize their motion accordingly and in real-time. We experimentally validate our coordination approach in a variety of scenarios and show that its performance scales to scenarios with hundreds of agents.
Proceedings of the AAAI Conference on Artificial Intelligence
Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation... more Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation and surveillance. Robots in such domains perform tasks at given locations and specific times, and are allocated tasks to optimize given team objectives. We propose an efficient, satisficing and centralized Monte Carlo TreeSearch based algorithm exploiting branch and bound paradigm to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. Unlike previous heuristics proposed for this problem, our approach offers theoretical guarantees and finds optimal solutions for some non-trivial data sets.
Multi-agent navigation methods typically assume that all agents use the same underlying framework... more Multi-agent navigation methods typically assume that all agents use the same underlying framework to navigate to their goal while avoiding colliding with each other. However, such assumption does not hold when agents do not know how other agents will move. We address this issue by proposing a Bayesian inference approach where an agent estimates the navigation model and goal of each neighbor, and uses this to compute a plan that minimizes collisions while driving it to its goal. Simulation experiments performed in many scenarios demonstrate that an agent using our approach computes safer and more time-efficient paths as compared to those generated without our inference approach and a state-of-the-art local navigation framework.
This paper discusses the current analysis of a large set of child-robot interaction experiments, ... more This paper discusses the current analysis of a large set of child-robot interaction experiments, in which a 2–4 year old child plays a set of games with a small humanoid robot, with the goal of detecting early signs of autism in toddlers based on the interactions. Our first goal in this paper is to condense and display these child-robot interactions as multi-channel time series, starting with the distances between the child, robot, parent, and experimenter. Our second goal is to use these data displays to compare and contrast different children, with the aim of clustering children with similar interaction patterns. Using a ceiling-mounted camera, we record the interaction between a child and a robot which performs different games and dances. After analyzing the video footage for the locations of all people and the robot in the room over the variable length of the interaction, we condense the interactions into simplified, quantifiable, scale-invariant data. We show how the distances ...
Proceedings of the AAAI Conference on Artificial Intelligence
We propose an auction algorithm to allocate tasks that have temporal constraints to cooperative r... more We propose an auction algorithm to allocate tasks that have temporal constraints to cooperative robots. Temporal constraints are expressed as time windows, within which a task must be executed. There are no restrictions on the time windows, which are allowed to overlap. Robots model their temporal constraints using a simple temporal network, enabling them to maintain consistent schedules. When bidding on a task, a robot takes into account its own current commitments and an optimization objective, which is to minimize the time of completion of the last task alone or in combination with minimizing the distance traveled. The algorithm works both when all the tasks are known upfront and when tasks arrive dynamically. We show the performance of the algorithm in simulation with different numbers of tasks and robots, and compare it with a baseline greedy algorithm and a state-of-the-art auction algorithm. Our algorithm is computationally frugal and consistently allocates more tasks than th...
Proceedings of the AAAI Conference on Artificial Intelligence
In crowded multi-agent navigation environments, the motion of the agents is significantly constra... more In crowded multi-agent navigation environments, the motion of the agents is significantly constrained by the motion of the nearby agents. This makes planning paths very difficult and leads to inefficient global motion. To address this problem, we propose a new distributed approach to coordinate the motions of agents in crowded environments. With our approach, agents take into account the velocities and goals of their neighbors and optimize their motion accordingly and in real-time. We experimentally validate our coordination approach in a variety of scenarios and show that its performance scales to scenarios with hundreds of agents.
Proceedings of the AAAI Conference on Artificial Intelligence
Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation... more Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation and surveillance. Robots in such domains perform tasks at given locations and specific times, and are allocated tasks to optimize given team objectives. We propose an efficient, satisficing and centralized Monte Carlo TreeSearch based algorithm exploiting branch and bound paradigm to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. Unlike previous heuristics proposed for this problem, our approach offers theoretical guarantees and finds optimal solutions for some non-trivial data sets.
Multi-agent navigation methods typically assume that all agents use the same underlying framework... more Multi-agent navigation methods typically assume that all agents use the same underlying framework to navigate to their goal while avoiding colliding with each other. However, such assumption does not hold when agents do not know how other agents will move. We address this issue by proposing a Bayesian inference approach where an agent estimates the navigation model and goal of each neighbor, and uses this to compute a plan that minimizes collisions while driving it to its goal. Simulation experiments performed in many scenarios demonstrate that an agent using our approach computes safer and more time-efficient paths as compared to those generated without our inference approach and a state-of-the-art local navigation framework.
This paper discusses the current analysis of a large set of child-robot interaction experiments, ... more This paper discusses the current analysis of a large set of child-robot interaction experiments, in which a 2–4 year old child plays a set of games with a small humanoid robot, with the goal of detecting early signs of autism in toddlers based on the interactions. Our first goal in this paper is to condense and display these child-robot interactions as multi-channel time series, starting with the distances between the child, robot, parent, and experimenter. Our second goal is to use these data displays to compare and contrast different children, with the aim of clustering children with similar interaction patterns. Using a ceiling-mounted camera, we record the interaction between a child and a robot which performs different games and dances. After analyzing the video footage for the locations of all people and the robot in the room over the variable length of the interaction, we condense the interactions into simplified, quantifiable, scale-invariant data. We show how the distances ...
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