… the website of the Learning, Artificial Intelligence, and Robotics Laboratory (LAIRLab) at Carnegie Mellon led by Drew Bagnell

Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey Carnegie Mellon University To appear at International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011). Link to Paper Motivated by a machine learning perspective|that game theoretic equilibria constraints should serve as guidelines for predicting agents’ strategies, we introduce maximum causal entropy correlated equilibria (MCECE), a [...]

3-D Scene Analysis via Sequenced Predictions over Points and Regions Xuehan Xiong, Daniel Munoz, J. Andrew Bagnell, Martial Hebert To appear: ICRA 2011. Preprint (pdf) We address the problem of understanding scenes from 3-D laser scans via per-point assignment of semantic labels. In order to mitigate the difficulties of using a graphical model for modeling [...]

Stacked Hierarchical Labeling Daniel Munoz, J. Andrew Bagnell, Martial Hebert To appear: ECCV 2010. Preprint (pdf) In this work we propose a hierarchical approach for labeling semantic objects and regions in scenes. Our approach is reminiscent of early vision literature in that we use a decomposition of the image in order to encode relational and [...]

Reinforcement Planning: RL for Optimal Planners Matt Zucker and J. Andrew Bagnell PDF Search based planners such as A* and Dijkstra’s algorithm are proven methods for guiding today’s robotic systems. Although such planners are typically based upon a coarse approximation of reality, they are nonetheless valuable due to their ability to reason about the future, [...]