Our research spans a wide range of topics: 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. Find more information about individual laboratories, projects, and publications below.
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.
We are interested in the control of complex movements in animals and robots. Biological movements can be modeled in detail using optimality principles - which is not surprising given that they are shaped by iterative optimization processes such as evolution, learning, adaptation. Similarly, the best way to engineer a complex control system is to specify a high-level performance criterion and leave the details to numerical optimization. In both areas, the main difficulty lies in actually performing the optimization. Thus our focus is on developing more powerful methods for optimal control and applying them to harder problems. A key tool we…
The lab's research focuses on understanding the brain using computational models and simulations and applying this knowledge to the task of building intelligent robotic systems and brain-computer interfaces (BCIs). The lab utilizes data and techniques from a variety of fields, ranging from neuroscience and psychology to machine learning and statistics. Current efforts are directed at: (1) understanding probabilistic information processing and learning in the brain, (2) building biologically inspired robots that can learn through experience and imitation, and (3) developing interfaces for controlling computers and robots using brain- and muscle-related signals.
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.
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 bioelectronics, robotics, and ubiquitous computing. Current research projects include: near field (non-radiative) wireless power transfer, far field (radiative) wirelessly powered sensing platforms, and novel sensors for robotic manipulation. One theme that runs through our work is machine sensing techniques that are not modeled on human perception. Another theme is the use of RF signals for sensing and power transfer, rather than communication. An additional recent theme is…
A summary of different research projects from all labs can be found here.
A list of recent publications can be found here.