Autonomous Decision and Control Laboratory

Algorithmic Contributions
 Scalable algorithms for partially observable Markov decision processes (POMDPs)
 Motion planning with safety guarantees
 Game theoretic algorithms

Theoretical Contributions
 Particle POMDP approximation bounds

Applications
 Space Domain Awareness
 Autonomous Driving
 Autonomous Aerial Scientific Missions
 Search and Rescue
 Space Exploration
 Ecology

Open Source Software
 POMDPs.jl Julia ecosystem
PI: Prof. Zachary Sunberg
PhD Students
Postdoc
POMDP Algorithms
Machine learning components handle highdimensional observations (e.g. images)
The ADCL's tree search algorithms solve realistic problems with continuous state, action and observation spaces.
The ADCL is a world leader in the development of scalable online algorithms for partially observable Markov decision processes (POMDPs)
ADCL Members developed the first general analytical bounds on particle belief approximation error, indicating that algorithms with complexity independent of the state and observation space size are possible.
See cuadcl.org/publications for related peerreviewed and submitted papers.
BOMCP
Voronoi Progressive Widening
POMCPOW
Applications
The algorithms that the ADCL develops are very general and effective in a wide range of applications including the following:
See cuadcl.org/publications for related peerreviewed and submitted papers.
Search and Rescue
 In collaboration with the COHRINT lab, we are developing a planner to automatically search an area for a lost or injured hiker with a multirotor aircraft.
Storm Science
 In collaboration with other RECUV researchers, we are developing algorithms for drones equipped with sensors to find paths within a storm that gather the most informative data.
Planetary Exploration
 In collaboration with NASA's jet propulsion lab, we are developing algorithms for safe autonomy for planetary exploration rovers.
Autonomous Driving
 Our algorithms can help autonomous vehicles safely interact with other road users such as pedestrians or humandriven cars.
Space Domain Awareness
 We have developed gametheoretic algorithms for deceptionrobust sensor tasking (next slide)
Explainable Decision Support
 In collaboration with the Johns Hopkins University Applied Physics Lab, we are developing methods for AI decision support algorithms to explain their conclusions and better understand what human operators want.
Ecology
 In collaboration with the University of California Berkeley, we have developed algorithms to plan rebuilding of ecosystems by adding key species sequentially.
Robotic Motion Planning
 In collaboration with other RECUV researchers, we have developed algorithms for safe motion planning under uncertainty.
Game Theory
A new application area for the ADCL is game theory. When rational agents have different goals, optimization can no longer represent their behavior. Instead, game theory provides a mathematical framework for reasoning about how rational agents interact.
 In partiallyobservable domains, agents with opposing goals may try to outsmart each other by acting unpredictably. This type of behavior is captured in the game theoretical concept of a mixed Nash equilibrium.
 Algorithms for calculating mixed Nash equilibria are mostly limited to tabletop games such as Poker  new algorithms are needed to find these solutions in physical problems.
 ADCL members have applied this theory to a space domain awareness game to calculate stochastic surveillance strategies (in the figure at left) that deter a satellite from taking action in a sensitive region (green) by increasing the probability of detection.
See cuadcl.org/publications for related peerreviewed and submitted papers.
ADCL Summary
By Zachary Sunberg
ADCL Summary
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