Welcome to new post-doctoral fellows

July 18, 2011

Dov Katz: working with Drew Bagnell and Tony Stentz on interactive perception for manipulation in unstructured environments. Moslem Kazemi: working with Nancy Pollard and Drew Bagnell on perception and force guided manipulation. Kris Katani: working with Martial Hebert and Drew Bagnell on activity prediction. Paul Vernaza: working with Drew Bagnell on compressed information space reasoning.

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Preprint: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

March 17, 2011

Stéphane Ross Geoffrey J. Gordon J. Andrew Bagnell, Carnegie Mellon University To Appear in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), 2011 Link to Paper Abstract: Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. [...]

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Preprint: Maximum Causal Entropy Correlated Equilibria for Markov Games

February 22, 2011

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 [...]

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Amusing New Dodge Commercial

February 20, 2011

Dodge has released an amusing new commercial referencing self-driving cars and other robotics advances. Well, those of us on Team Robot take it as a compliment.

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Preprint: 3-D Scene Analysis via Sequenced Predictions over Points and Regions

January 31, 2011

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 [...]

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Merry Christmas and Happy Holidays!

December 11, 2010

Andy, our ARM (Autonomous Robot Manipulation) wishes you a merry christmas! Mihail Pivtoraiko, our motion planning for manipulation expert, demonstrates Andy’s current dexterity.

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Preprint: Stacked Hierarchical Labeling

July 5, 2010

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 [...]

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Congrats to Brian! Best Paper Runner up: Modeling Interaction via the Principle of Maximum Causal Entropy

June 20, 2010

Modeling Interaction via the Principle of Maximum Causal Entropy ICML runner up for best student paper by Brian Ziebart, J. Andrew Bagnell, and Anind Dey. @inproceedings{bziebart-maxcausalent, author = {Brian D. Ziebart and J. Andrew Bagnell and Anind K. Dey}, title = {Modeling Interaction via the Principle of Maximum Causal Entropy}, year = {2010}, booktitle = [...]

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Preprint: Reinforcement Planning: RL for Optimal Planners

April 20, 2010

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, [...]

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Preprint: Modeling Interaction via the Principle of Maximum Causal Entropy

April 19, 2010

Modeling Interaction via the Principle of Maximum Causal Entropy Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey To appear: ICML 2010. Preprint (pdf) The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distributions with elements of interaction and feedback where [...]

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