by sross on March 22, 2010
A new paper from the project set to appear at AISTATS 2010:
S. Ross and J. A. Bagnell. Efficient Reductions for Imitation Learning. To appear in Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2010. [pdf] [supplementary material]
Abstract: Imitation Learning, while applied successfully on many large real-world problems, is typically addressed as a standard supervised learning problem, where it is assumed the training and testing data are i.i.d.. This is not true in imitation learning as the learned policy influences the future test inputs (states) upon which it will be tested. We show that this leads to compounding errors and a regret bound that grows quadratically in the time horizon of the task. We propose two alternative algorithms for imitation learning where training occurs over several episodes of interaction. These two approaches share in common that the learner’s policy is slowly modified from executing the expert’s policy to the learned policy. We show that this leads to stronger performance guarantees and demonstrate the improved performance on two challenging problems: training a learner to play 1) a 3D racing game (Super Tux Kart) and 2) Mario Bros.; given input images from the games and corresponding actions taken by a human expert and near-optimal planner respectively.
by Alex Grubb on February 17, 2010
There are a number of papers from the project set to appear at ICRA 2010 in May:
P. Henry, C. Vollmer, B. Ferris, and D. Fox. Learning to navigate through crowded environments. To appear in Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 2010. [pdf]
Abstract: The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side walks. The key research question addressed in this paper is how to learn planners that generate human-like motion behavior. Our approach uses inverse reinforcement learning (IRL) to learn human-like navigation behavior based on example paths. Since robots have only limited sensing, we extend existing IRL methods to the case of partially observable environments. We demonstrate the capabilities of our approach using a realistic crowd flow simulator in which we modeled multiple scenarios in crowded environments. We show that our planner learned to guide the robot along the flow of people when the environment is crowded, and along the shortest path if no people are around.
B. Tovar and S. M. LaValle. Searching and mapping among indistinguishable convex obstacles. To appear in Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 2010. [pdf]
Abstract: This paper considers a robot that moves in the plane and is only able to sense the cyclic order of landmarks with respect to its current position. No metric information is available regarding the robot or landmark positions; moreover, the robot does not have a compass or odometers (e.g., coordinates). We carefully study the information space of the robot, and establish its capabilities in terms of mapping the environment and accomplishing tasks, such as navigation and patrolling. When the robot moves exclusively inside the convex hull of the set of landmarks, the information space can be nicely characterized as an order type, which provides information powerful enough to determine which points lie inside the convex hulls of subsets of landmarks. Additionally, if the robot is allowed to move outside the convex hull of the set of landmarks, the information space is described with a swap cell decomposition, which is an aspect graph in which each aspect is a cyclic permutation of landmarks. We show how to construct such decomposition through its dual, using two kinds of feedback motion commands based on the landmarks sensed.
J. Yu and S. M. LaValle. Probabilistic shadow information spaces. To appear in Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 2010. [pdf]
Abstract: This paper introduces a Bayesian ?lter that is specifically designed for counting targets that move outside of the field of view while performing a sensor sweep. Information space concepts are used to dramatically reduce the filter complexity so that information is processed only when the shadow region (all points invisible to the sensors) changes combinatorially or targets pass in and out of view. Previous work assumed perfect observations; however, this paper extends the approach to enable probabilistic disturbances. Practical algorithms are introduced, implemented, and demonstrated for computing the filter outputs based on realistic data.
You can see more publications from the RIRIS project here.
