Location

Date: Monday, November 3 2014 (9:30 AM – 4:30 PM)
Place: Gates-Hillman Center 2109 , 5000 Forbes Ave, Pittsburgh PA 15213 [map]

Please join us for the 2014 Review.  The schedule is available here, but it is not yet finalized.

Note room change!:

Location

Date: Tuesday, November 19 2013 (9:30 AM – 4:30 PM)
Place: Newell Simon NSH 3305 , 5000 Forbes Ave, Pittsburgh PA 15213 [map]

Please join us for the Year 5 Program Review and planning session!
More details and registration here

The year 4 review has been rescheduled to Thursday, December 13th. See the schedule for more details.

Please be sure to let Karen know if you are attending so we can plan accordingly.

Canceled due to weather!

Stay tuned for rescheduling information.

The location and schedule for the fourth year review has been posted here. Details will be filled in shortly.

Please be sure to let Karen know if you plan on attending so we can plan accordingly.

The schedule for the third year review has been posted here.

D. S. Yershov and S. M. LaValle. Simplicial Dijkstra and A* Algorithms for Optimal Feedback Planning. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2011. [pdf]

Abstract:

This paper considers the Euclidean shortest path problem among obstacles in Rn. Adaptations of Dijkstra’s and A* algorithms are introduced that compute the approximate cost-to-go function over a simplicial complex embedded in the free space. Interpolation methods are carefully designed and analyzed so that they are proven to converge numerically to the optimal cost-to-go function. As the result, the computed function produces approximately optimal trajectories. The methods are implemented and demonstrated on 2D and 3D examples. As expected, the simplicial A* algorithm significantly improves performance over the simplicial Dijkstra’s algorithm.

K. Waugh, Brian D. Ziebart and J. A. Bagnell. Computational Rationalization: The Inverse Equilibrium Problem. Proceedings of the International Joint Conference on Machine Learning (ICML), 2011. [pdf]

Abstract:

Modeling the behavior of imperfect agents from a small number of observations is a difficult, but important task. In the single-agent decision-theoretic setting, inverse optimal control has been successfully employed. It assumes that observed behavior is an approximately optimal solution to an unknown decision problem, and learns the problem’s parameters that best explain the examples. The inferred parameters can be used to accurately predict future behavior, describe the agent’s preferences, or imitate the agent’s behavior in similar unobserved situations. In this work, we consider similar tasks in competitive and cooperative multi-agent domains. Here, unlike single-agent settings, a player cannot myopically maximize its reward — it must speculate on how the other agents may act to influence the game’s outcome. Employing the game-theoretic notion of regret and the principle of maximum entropy, we introduce a technique for predicting and generalizing behavior, as well as recovering a reward function in these domains.

S. Bhattacharya, M. Likhachev and V. Kumar. Identification and Representation of Homotopy Classes of Trajectories for Search-based Path Planning in 3D. Proceedings of Robotics: Science and Systems Conference (RSS), 2011. [pdf]

Abstract:

There are many applications in motion planning where it is important to consider and distinguish between different homotopy classes of trajectories. Two trajectories are homotopic if one trajectory can be continuously deformed into another without passing through an obstacle, and a homotopy class is a collection of homotopic trajectories. In this paper we consider the problem of robot exploration and planning in three-dimensional configuration spaces to (a) identify and classify different homotopy classes; and (b) plan trajectories constrained to certain homotopy classes or avoiding specified homotopy classes. In previous work [1] we have solved this problem for two-dimensional, static environments using the Cauchy Integral Theorem in concert with graph search techniques. The robot workspace is mapped to the complex plane and obstacles are poles in this plane. The Residue Theorem allows the use of integration along the path to distinguish between trajectories in different homotopy classes. However, this idea is fundamentally limited to two dimensions. In this work we develop new techniques to solve the same problem, but in three dimensions, using theorems from electromagnetism. The Biot-Savart law lets us design an appropriate vector field, the line integral of which, using the integral form of Ampere’s Law, encodes information about homotopy classes in three dimensions. Skeletons of obstacles in the robot world are extracted and are modeled by current- carrying conductors. We describe the development of a practical graph-search based planning tool with theoretical guarantees by combining integration theory with search techniques, and illustrate it with examples in three-dimensional spaces such as two-dimensional, dynamic environments and three-dimensional static environments.

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.

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.