Robotics researchers at the Paul G. Allen School of Computer Science
& Engineering are engaged in ground-breaking work in mechanism
design, sensors, computer vision, robot learning, Bayesian state
estimation, control theory, numerical optimization, biomechanics,
neural control of movement, computational neuroscience,
brain-machine interfaces, natural language instruction,
physics-based animation, mobile manipulation, and human-robot
interaction. We are currently working to define large-scale joint
initiatives that will enable us to leverage our multi-disciplinary
expertise to attack the most challenging problems in field.
In the Human-Centered Robotics lab we aim to develop
robotics that are useful and usable for future users of
task-oriented robots. To that end we take a human-centered approach
by working on robotics problems that would enable functionalities
that are most needed to assist people in everyday tasks, while also
making state-of-the-art robotics functionalities accessible to users
with no technical knowledge. Our projects focus on end-user robot
programming, robotic tool use, and assistive robotics.
The mission of the Personal Robotics Lab is to develop the fundamental
building blocks of perception, manipulation, learning, and human-robot
interaction to enable robots to perform complex physical manipulation
tasks under clutter and uncertainty with and around people.
The RSE-Lab was established in 2001. We are interested in the
development of computing systems that interact with the physical world
in an intelligent way. To investigate such systems, we focus on
problems in robotics and activity recognition. We develop rich yet
efficient techniques for perception and control in mobile robot
navigation, map building, collaboration, and manipulation. We also
develop state estimation and machine learning approaches for areas
such as object recognition and tracking, human robot interaction, and
human activity recognition.
We perform fundamental and applied research in machine learning, artificial
intelligence, and robotics with a focus on developing theory and systems that
tightly integrate perception, learning, and control. Our work touches on a
range of problems including computer vision, state estimation, localization
and mapping, high-speed navigation, motion planning, and robotic manipulation.
The algorithms that we develop use and extend theory from deep learning and
neural networks, nonparametric statistics, graphical models, nonconvex
optimization, quantum physics, online learning, reinforcement learning,
and optimal control.
In the Sensor Systems Laboratory we invent new sensor systems, devise
innovative ways to power and communicate with them, and develop algorithms
for using them. Our research has applications in the domains of ubiquitous
computing, bioelectronics, and robotics. We have developed a series of
pre-touch sensors for robot manipulation. The sensors allow the fingers
to detect an object before contact. Proximity information is provided in
the local coordinate frame of the fingers, eliminating errors that arise when
sensors and actuators are in different frames. In addition to developing
the sensors themselves, we are interested in algorithms for estimation,
control, and manipulation that make use of the novel sensors. Our primary
robotics research platform is our lab's dedicated PR2 robot; we replace the
PR2's fingers with our own custom fingers, so that they can be used like sensors
native to the PR2. We have worked on robot chess, a robot that solves the
rubik's cube, and we have an increasing interest in robots that play games with people.
The WEIRD (Washington Embodied Intelligence and Robotics Development) lab is interested
in robotic learning problems. Currently we are thinking deeply about reinforcement learning
algorithms to enable real-world robotic manipulation tasks in the home. Our interests also
span computer vision, language modeling, human-robot interaction and broader notions of robustness
and reliability in machine learning systems.