|OCCaM is a new lab at Olin College. My name is Paul Ruvolo, and I am the director of the lab as well as a new faculty member in Computing. If any of the information below sparks your interest, please come visit me in MH357 (see the Recruiting section below for more details).|
We are interested in the creation of intelligent machines. By intelligent we mean capable of flexibly and adaptively solving a wide array of tasks that do not have obvious solutions. By machines we mean a variety of things such as robots, mobile computing devices, and software programs. By we, I currently mean I (since I am still in the process recruiting my first group of students). While the quest for creating artificial intelligence has long been a goal of computer science, a number of recent trends (both hardware and software) have made the present a time of nearly unlimited promise for the advancement of AI research. The trends driving our optimism include:
These trends enable motivated students to contribute to the goal of developing intelligent machines through research in areas such as algorithms and analysis, software development, electrical and computer engineering, design, and data analysis.
Current Research Focus: Crowdsourcing Sensorimotor Intelligence for Assistive Machines
This project starts from the following premise: there is a huge pool of people that want to volunteer to help others, but don't due to a variety of factors including: the difficulty of investigating where their talents can be utilized, physically commuting to the location for volunteering, and finding times where they are free and their services are needed. Wouldn't it be great if we could overcome all of these limitations and thus free up this huge untapped pool of goodwill? We are working to harness cutting edge computer science to do just that by leveraging human volunteers to power intelligent machines for helping disabled populations.
Consider this video detailing the experience of a blind person going shopping in a grocery store. In the video, a blind person receives help while shopping from a grocery store employee. However, this service is not available at every store, and working with the employee often makes the blind person feel uncomfortable.
We are working to create computer-based assistive technologies to help the blind shop. Our current design is to utilize a pair of goggles that the blind person wears that are outfitted with a camera. The camera will be continuously streaming video and audio back to a sighted volunteer. The volunteer will help the blind person identify particular objects of interest based upon the stream of video images coming across the camera feed. Once the objects are identified, through a combination of automatic low-latency computer vision-based guidance and additional input from the volunteer the system will help the blind person approach and grasp the object effectively.
Picture from: http://grozi.calit2.net/
Connections to Industry: There are several computer vision companies focusing on technology such as logo recognition which pertain to this project. One such company is Orpix who develop logo recognition, vehicle recognition, and other computer vision solutions. We plan on collaborating with such companies when appropriate.
Future Directions: another major class of agents (besides people with sensory impairments) that require sensorimotor assistance is robots. A major obstacle to deploying robots as assistants in the home is that modules for object recognition, grasping, and manipulation do not work well in the cluttered, unconstrained environments, and highly variable environment of the home. The next phase of this project will be to partner with the folks at Olin Robotics to leverage this crowdsourced approach to sensorimotor intelligence to provide help to an assistive robot rather than to a human.
Other Research Interests: here at OCCaM, we try to keep an open mind. In my experience this is necessary to do research. In this spirit, if you have a particular research direction you want to explore that is related to my interests I would love to talk. To get your creative juices flowing, below is a list of some other projects of mine. To view information on the papers cited below, cross-reference the citation number (e.g. ) with the publications section of the About Paul Ruvolo page.
Lifelong Machine Learning
Traditional machine learning algorithms learn tasks in isolation. While this works well given sufficient data computational power, in the case where these conditions are not met we want to be able to learn tasks efficiently in sequence while both building upon and refining previously learned knowledge. In previous work, , we have shown that we can get near identical performance to competing methods and achieving about a 1000x speedup while providing various theoretical guarantees.
Quality Control Methods for Crowdsourced Data
How to best combine input from multiple people performing some crowdsourced task is an active area of research. Issues include how to automatically assess participant expertise, instance difficult, and participant specialization. In previous work, , I have derived various machine learning based methods for automatic quality control of crowdsourced data.
Inverse Optimal Control and Goal-Based Imitation
A very hot topic in machine learning is inferring the goals of an agent from instances of their behavior. These goals are typically formalized as particular performance objectives that an agent may be trying to achieve. It has been shown that goal-inference can serve as a very effective way to allow a robot to learn skills from a human. As part of my thesis, , I developed several new techniques to infer goals both as a means of skill transfer and as a means of explaining the behavior of people. My aim is to publish this in a good conference, but I need to run some good experiments first (perhaps you are interested in helping?).
I have worked on machine learning methods for: recognizing auditory categories such as crying and emotion in human speech , localizing audio , fusing auditory and visual information , and recognizing facial expressions .
At UC San Diego I did research on a project to try to program a robotic infant that would learn in a manner similar to how human infants learn -- through experience interacting with its environment. In this project we investigated various tools from optimal control as a means to allow a robot to autonomously learn to more effectively interact with the physical and social world. My relevant publications on this topic are: .
Computational Analysis of Behavior
I collaborate with Daniel Messinger's developmental psychology lab at the University of Miami. In our research, , we apply cutting edge machine learning techniques to the task of characterizing and understanding infant social and motor development. Projects based on our work are still flourishing at the University of Miami. For this reason, Olin students have ample opportunity to work as part of an interdisciplinary team in concert with the scientists in Daniel Messinger's lab.
Preschool has been shown to be one of the most crucial times for children's learning. In the past, , I have worked on creating a robotic teaching aide to help 18-24 month old children learn new vocabulary.
Lab Meetings: once we have a critical mass of members, we will setup a weekly lab meeting.
The Olin Machine Learning Reading Group will meet weekly to discuss machine learning opportunities at Olin College. In terms of personnel, this group may have significant overlap with OCCaM.
I am new to Olin. Maybe you would be interested in helping me found my lab :). If the research described above sounds interesting to you or if you just want to learn more about machine learning, then please come by my office (Milas Hall 357) and let’s chat! Also, please see my expectations page to learn more about what working with me on a research project entails.
In addition to directing OCCaM, I am also a co-PI on an Office of Naval Research (ONR) grant investigating computational approaches to lifelong learning. Myself and the other PI on this grant, Eric Eaton, are currently looking to hire a postdoc who will be based at the University of Pennsylvania. The position offers a generous salary, money for travel, and career mentorship.