My research focus is on controlling teams of robots to collect information about physical processes of interest in applications such as environmental monitoring, security and surveillance, localization and mapping, construction and structure inspection, agriculture, and search and rescue. The message of my work is that transitioning from static, manual data collection to autonomous robot deployments will result in accurate and efficient sampling, higher-fidelity models, reduced human supervision, access to dangerous areas, and adaptation to environment conditions. My research has contributed towards this vision by focusing on control and estimation techniques for multi-robot * active information acquisition* and on

Information acquisition is the process of estimating the state of observed phenomena of interest by utilizing the available sensing modalities. Active information acquisition, on the other hand, is the process of controlling a team of **n** sensing robots in order to improving the accuracy and efficiency of the estimation process over a planning horizon of **T** steps. It couples the estimation problem with a stochastic control problem, whose objective function is an *information measure*, used to judge the informativeness of the planned robot trajectories. The main challenge is to design coupled estimation-control techniques that model the evolution of the robots, targets and measurements accurately and are scalable in both **n** and **T**.

**Multi-robot Active SLAM (Video 1)**

A simultaneous localization and mapping (SLAM) problem, in which the task of **n** robots is to autonomously explore the environment and map landmarks of interest, while staying well localized, can be cast as an active information acquisition problem. The goal is to design control policies for the robots that, e.g., minimize the entropy of the joint map-robot state. The original problem with nonlinear motion and observation models can be converted into a linear Gaussian one via statistical linearization and model predictive control. We proved that the classic * separation principle* between estimation and control holds for the linear Gaussian active information acquisition problem. As a result, the optimal estimator is the Kalman filter, the entropy is proportional to the log-determinant of the covariance matrix, and the problem can be reduced to deterministic optimal control. Still, the complexity of computing the optimal open-loop policy scales exponentially with the planning horizon

**Environmental Monitoring (Video 2)**

Environmental monitoring is another task of practical importance that can be addressed via active information acquisition. Consider the problem of localizing the source of a physical signal of interest, such as magnetic force, heat, radio or chemical concentration, using a robot team. My collaborators and I proposed * distributed control strategies for source seeking* specific to two scenarios: one in which the robots have a noisy model of the signal formation process and one in which a signal model is not available. In the model-free scenario, the robot team follows a stochastic gradient of the signal field. Our approach is

**Active Object Recognition (Video 3, Video 4)**

While a large class of active information gathering problems can be handled via linearization and model predictive control, the approach cannot be used when some measurement or state variables are discrete (e.g., scene labels, object classes, medical hypotheses). Such complications arise in active vision, classification, and hypothesis testing problems. In these cases, * non-greedy closed-loop planning* is needed to solve the stochastic active information acquisition problem. We proposed an exact planning algorithm based on dynamic programming that obtains the optimal solution but scales poorly with the size of the state and measurement spaces. To provide scalability, we developed an approximation algorithm based on

**Video 1: Decentralized multi-robot active SLAM**

**Video 2: Wireless radio source seeking**

**Video 3: Active object recognition with a mobile depth camera**

**Video 4: Active deformable part models for faster object detection**

A complementary direction to developing scalable information-seeking techniques is devising better representations for localization, mapping, and navigation. The recent impressive progress in robotics is due in large part to the advances in inference techniques based on graphical models and simultaneous localization and mapping (SLAM) algorithms. For example, flying a quadrotor away from the motion capture system would not be possible without SLAM, just like Google's Project Tango would be nothing more than an Andorid phone. While existing SLAM approaches are quite mature, they rely heavily on (continuous Gaussian) geometric features such as points, lines, and planes and cannot handle (discrete) semantic information such as recognized objects in the robot's surroundings.

Our contribution is a sensor measurement model for set-valued observations that captures both metric and semantic information and * incorporates missed and false detections and unknown data association*. We proved that obtaining the likelihood of a set-valued observation is equivalent to a

**Video 5: Vehicle localization in a residential area using object recognition**

**Video 6: Simultaneous localization and mapping using semantic information**

**Video 7: Mobile robot localization using object recognition**