Object Recognition Database for Robotic Grasping

Object recognition and manipulation are critical in enabling robots to interact with objects in a household environment. Construction of 3D object recognition databases is time and resource intensive, often requiring specialized equipment, and is therefore difficult to apply to robots in the field. This project focuses on techniques for constructing object models for 3D object recognition and manipulation through the use of crowdsourcing and web robotics. The database consists of point clouds generated using a novel iterative point cloud registration algorithm, which includes the potential to encode manipulation data and usability characteristics.


DARPA Robotics Challenge

The DARPA Robotics Challenge seeks to develop ground robots capable of executing complex tasks in dangerous, degraded, human-engineered environments, particularly in the area of disaster response. The RAIL research group is part of the 10-university Track A DRC-HUBO team led by Drexel University. The project is a collaboration with Dmitry Berenson and Rob Lindeman at WPI. Our work focuses on user-guided manipulation framework for high degree-of-freedom robots operating in environments with limited communication.

  • Nicholas Alunni, Calder Phillips-Grafflin, Halit Bener Suay, Daniel Lofaro, Dmitry Berenson, Sonia Chernova, Robert W. Lindeman and Paul Oh. Toward A User-Guided Manipulation Framework for High-DOF Robots with Limited Communication, In the IEEE International Conference on Technologies for Practical Robot Applications (TePRA), 2013.


Collaborative Robot Learning from Demonstration Using Hierarchical Task Networks and Attributive Motion Planning

The objective of this work is to scale up robot learning from demonstration (LfD) to larger and more complex tasks than currently possible in order to enable robots to move out of highly constrained, repetitive applications, such as manufacturing, and into human-oriented environments with greater autonomy. The main focus of our approach is to introduce hierarchical task networks (HTNs) and associated shared mental models (between a human teacher and robot learner) into the LfD paradigm. We will apply collaborative discourse theory to design a more natural and effective interaction between the human teacher and robot learner. Our approach will leverage motion planning to ground the HTN model in the real world in order to effectively deal with the variability of unconstrained human-oriented environments.


Message Authentication Codes for Secure Remote Non-Native Client Connections to ROS Enabled Robots

Recent work in the robotics community has lead to the emergence of cloud-based solutions and remote clients. Such work allows robots to effectively distribute complex computations across multiple machines, and allows remote clients, both human and automata, to control robots across the globe. With the increasing use and importance of such ideas, it is crucial not to overlook the critical issue of security in these systems. This project demonstrates the use of web tokens for achieving secure authentication for remote, non-native clients in the widely-used Robot Operating System (ROS) middleware. The software is written in a system-independent manner and is available in open source on GitHub.



RobotsFor.Me architectureThis project aims to develop a web-based, platform-independent tool for conducting large-scale robotics user studies online. Building upon the open source PR2 Remote Lab, RobotsFor.Me (http://RobotsFor.me) enables users to remotely control robots through a common web browser, enabling researchers to conduct user studies with participants across the globe. Still under development, the current version of RobotsFor.Me includes a web interface for robot control, as well as a Robot Management System for managing users and different study conditions. The complete system is platform independent, and to date has been tested with the Rovio, youBot and PR2 platforms in both physical and simulated environments. RobotsFor.Me is the first robot remote lab designed for use by the general public.

  • Russell Toris and Sonia Chernova. RobotsFor.Me and Robots For You. Interactive Machine Learning Workshop, Intelligent User Interfaces Conference, 2013.
  • Russell Toris, David Kent, Sonia Chernova. "The Robot Management System: A Framework for Conducting Human-Robot Interaction Studies Through Crowdsourcing", Journal on Human-Robot Interaction (to appear).
  • National Geographic, "Robot Revolution? Scientists Teach Robots to Learn."

Robot Learning from Demonstration

Nao Robot

Robot learning from demonstration (LfD) research focuses on algorithms that enable a robot to learn new task policies from demonstrations performed by a human teacher.  See the Survey of Robot Learning from Demonstration for more information on this research area.  Our current work includes the first comparative evaluation of leading algorithms in this area and the development of new multi-strategy learning algorithms:

  • Halit Bener Suay, Russell Toris and Sonia Chernova.  A Practical Comparison of Three Robot Learning from Demonstration Algorithms.  International Journal of Social Robotics, special issue on Learning from Demonstration, Volume 4, Issue 4, Page 319-330, 2012.
  • Osentoski S., Pitzer B., Crick C., Graylin J., Dong S., Grollman D., Suay H.B., and Jenkins O.C. Remote Robotic Laboratories for Learning from Demonstration. In International Journal of Social Robotics, special issue on Learning from Demonstration, Volume 4, Issue 4, Page 449-461, 2012.
  • Halit Bener Suay, Joseph Beck, Sonia Chernova. Using Causal Models for Learning from Demonstration. AAAI Fall Symposium on Robots Learning Interactively from Human Teachers, 2012.
  • Halit Bener Suay and Sonia Chernova. A Comparison of Two Algorithms for Robot Learning from Demonstration. In the IEEE International Conference on Systems, Man, and Cybernetics, 2011.
  • Halit Bener Suay and Sonia Chernova. Effect of the Human Guidance and State Space Size on Interactive Reinforcement Learning. In the IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man), 2011.

Cloud Primer: Leveraging Common Sense Computing for Early Childhood Literacy

CloudPrimer Screenshot

Providing young children with opportunities to develop early literacy skills is important to their success in school, their success in learning to read, and their success in life. This project focuses on the creation of a new interactive reading primer technology on tablet computers that will foster early literacy skills and shared parent-child reading through the use of a targeted discussion-topic suggestion system aimed at the adult participant.  The Cloud Primer will crowdsource the interactions and discussions of parent-child dyads across a community of readers. It will then leverage this information in combination with a common sense knowledge base to develop computational models of the interactions. These models will then be used to provide context-sensitive discussion topic suggestions to parents during the shared reading activity with young children.

  • Adrian Boteanu and Sonia Chernova. Modeling Discussion Topics in Interactions with a Tablet Reading Primer. International Conference on Intelligent User Interfaces, 2013.
  • Adrian Boteanu and Sonia Chernova. Modeling Topics in User Dialog for Interactive Tablet Media. Workshop on Human Computation in Digital Entertainment at the Eighth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2012.

Human-Agent Transfer

Human-Agent Transfer (HAT) is a policy learning technique that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains.  Using this technique we can effectively transfer knowledge from a human to an agent, even when they have different perceptions of state.

  • Matthew Taylor, Halit Bener Suay and Sonia Chernova. Integrating Reinforcement Learning with Human Demonstrations of Varying Ability. In the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Taipei, Taiwan, 2011.
  • Matthew E. Taylor, Halit Bener Suay and Sonia Chernova. Using Human Demonstrations to Improve Reinforcement Learning. In the AAAI 2011 Spring Symposium: Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Palo Alto, CA, 2011.

RoboCup Autonomous Robot Soccer

RoboCupRoboCup is an international competition that aims to promote AI and robotics research through the development of autonomous soccer playing robots. In 2010 and 2011 WPI competed in the Standard Platform League, which requires all teams to use the Aldebaran Nao robots. The robots are not remote controlled in any way; they observe the world through two head-mounted cameras and use this information to recognize objects in the environment and their own location on the field. Robots communicate with each other using the wireless network and use on-board processing to decide which actions to take.  Here is an article describing the event and WPI Warriors team.


Open Source Kinect Interface for Humanoid Robot Control

The ROS Nao-OpenNI package provides gesture-based control for humanoid robots using the Microsoft Kinect sensor. The video on the right shows the code being used to control an Aldebaran Nao.

© 2013 RAIL Suffusion theme by Sayontan Sinha