Learning Rich, Tractable Models of the Real World
Progress Report: July 1, 2000December 31, 2000
Leslie Pack Kaelbling
The everyday world of a household or a city street is exceedingly complex and dynamic, from a robot's perspective. In order for robots to operate effectively in such domains, they have to learn models of how the world works and use them to predict the effects of their actions. In traditional AI, such models were represented in first-order logic and related languages; they had no representation of the inherent uncertainty in the world and were not connected up to real perceptual systems. More recent AI techniques allow model-learning directly from perceptual data, but they are representationally impoverished, lacking the ability to refer to objects as such, or to make relational generalizations of the form: "If object A is on object B, then if I move object B, object A will probably move too."
We are engaged in building a robotic system with an arm and camera (currently, in simulation) that will learn relational models of the environment from perceptual data. The models will capture the inherent uncertainty of the environment, and will support planning via sampling and simulation.
Progress Through December 2000
In September, 2000, we added a postdoctoral researcher, Tim Oates, and two research assistants, Natalia Hernandez and Sarah Finney to the project. During these three months, we have explored a wide range of issues related to relational model learning. In particular, we have studied the use of deictic expressions, both in propositional and in relational representations. In addition, we have designed an architecture for goal-directed model-learning and have implemented most of the algorithmic components. We summarized our work, with a detailed discussion of a number of design issues in the project, in a report titled "Learning in Worlds with Objects", which is available on the project web site and will be presented at the AAAI Stanford Spring Symposium in March, 2001.
A list of concrete achievements is as follows:
Research Plan for the Next Six Months
We are currently integrating the implementations described above. We expected to have empirical results very soon that demonstrate the utility of deictic representations, even in fairly simple domains. Concretely, our plan is to: