We are releasing a teaser data set of N objects for the Million Object Challenge. This data consists of objects mapped using Baxters at our site. We include objects from the YCB data set, as well as other objects arranged into several object categories.
Slashdot wrote a little article about us! Neato! The title may be a bit inaccurate, but it does talk about a cool collaboration we have with Cornell, particularly our fellow roboticist Ashutosh Saxena.
We are excited to announce the 4th edition of NERC. It will be held on Saturday, November 7th at WPI. More details are at http://nerc.mit.edu. We have an exciting lineup of speakers including Leslie Kaelbling, Drew Bennett, and Sangbae Kim.
It’s hard to believe that NERC is four years old. It’s not what I expected when we founded the event back in 2012. It’s great to see so much energy and excitement around robotics in the Northeast!
We are announcing the release of BurlapCraft 1.1. BurlapCraft is a mod for Minecraft that allows an autonomous agent to be controlled by BURLAP, the Brown-UMBC Reinforcement Learning and Planning Library. Performing experimental research on robotic platforms involves numerous practical complications, while studying collaborative interactions and efficiently collecting data from humans benefit from real time response. To circumvent these complications, we have created a Minecraft mod called BurlapCraft which enables the use of the reinforcement learning and planning library BURLAP to model and solve different tasks within Minecraft. BurlapCraft makes reinforcement learning and planning easier in three core ways:
the underlying Minecraft environment makes the construction of experiments simple for the developer and so allows the rapid prototyping of experimental setup;
BURLAP contributes a wide variety of extensible algorithms for learning and planning, allowing easy iteration and development of task models and algorithms;
the familiarity and ubiquity of Minecraft makes it easy to recruit and train users yet includes very challenging tasks which are unsolvable by existing planners.
To validate BurlapCraft as a platform for AI development, we have demonstrated the execution of A*, BFS, RMax, language understanding, and learning language groundings from user demonstrations in five Minecraft “dungeons.”
Here is a video of a Minecraft agent being trained to understand natural language commands from examples:
The technical approach we’ve taken to language learning is described here.
Try it now! You can try it out by downloading our mod jar file and following these instructions to teach the agent more commands. You can also find source code and detailed instructions on installing and using the mode here.
Our paper describing the mod was recently published in the Artificial Intelligence for Human-Robot Interaction AAAI Fall Symposium :
Krishna Aluru, Stefanie Tellex, John Oberlin, and James MacGlashan. Minecraft as an experimental world for AI in robotics. In AAAI Fall Symposium, 2015.
More information about this project is here. You can also read about our work on learning to plan in large state spaces like Minecraft.
Our work outlines challenges that will motivate the development of better planning and learning algorithm techniques that can be quickly validated and compared to other work. In the future, we would like to be able to leverage the user base that comes with Minecraft to collect new and interesting data for such tasks and develop algorithms to solve them.
On Friday we visited Rethink Robotics to install our software stack on their robots. They have agreed to help out with our scanning project. We calibrated three of the four arms; the fourth wrist camera had some kind of problem (perhaps hardware?) that led to an image that was too noisy to be useful. We have never gotten to use two robots at once before, and it was exciting to see them both moving and scanning objects. Our scanning stack is rapidly maturing, and we are on our way to scanning one million objects! More information about the research project is available in our Blue Sky Ideas paper.