Footage of some preliminary results obtained at UC Berkeley which were compiled for Willow Garage's One-Minute PR2 Quick Start Video Contest. The PR2 is presented with two socks. It then classifies each sock as either "inside" or "outside" and flips accordingly. Once both socks are in the proper orientation, it pairs them. Team: Ping Chuan (Ted) Wang, Stephen Miller, Mario Fritz, Trevor Darrell, Pieter Abbeel.

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Project was led by UC Berkeley Ph.D. student Jeremy Maitin-Shepard working with Professor Pieter Abbeel. The video is shown at 50X speed. More details can be found in the paper at www.cs.berkeley.edu

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Perception for the Manipulation of Socks - Ping Chuan Wang, Stephen Miller, Mario Fritz, Trevor Darrell, Pieter Abbeel. Presented at IROS 2011 More information can be found at rll.berkeley.edu

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Project was led by UC Berkeley Ph.D. student Jeremy Maitin-Shepard working with Professor Pieter Abbeel. The video is shown at 30X speed. More details can be found in the paper at www.cs.berkeley.edu

robot autonomous deformable object manipulation towel folding laundry university california berkeley aritifical intelligence AI machine learning pr2 willow garage maitin-shepard abbeel rllberkeley

Footage of some preliminary results obtained at UC Berkeley which were compiled for Willow Garage's One-Minute PR2 Quick Start Video Contest. The PR2 picks up a towel from an unknown configuration and proceeds to fold it. When it is unsure which corner would be best to grasp, it polls Twitter for advice. In return, once it has finished the procedure, it posts a photo of the folded towel. To interact with the PR2 and assist it in future demos, follow now at twitter.com Team: Stephen Miller, Mario Fritz, Marco Cusumano-Towner, Pieter Abbeel.

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The Berkeley PR2 autonomously pairs socks. This includes: identifying inside-out socks, folding, matching, and bunching them together. This work was done by Ping Chuan Wang, Stephen Miller, Mario Fritz, Trevor Darrell, and Pieter Abbeel, at the University of California, Berkeley. Video shown at 15x speed. Details may be found in the paper at www.cs.berkeley.edu

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Many robotic control tasks involve complex dynamics that are hard to model. Hand-specifying trajectories that satisfy a system's dynamics can be very time-consuming and often exceedingly difficult. We present an algorithm for automatically generating large classes of trajectories for difficult control tasks by learning parameterized versions of desired maneuvers from multiple expert demonstrations. Our algorithm has enabled the successful execution of several parameterized aerobatic maneuvers by our autonomous helicopter.

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EG-RRT: Environment-Guided Random Trees for Kinodynamic Motion Planning with Uncertainty and Obstacles - Leonard Jaillet, Judy Hoffman, Jur van den Berg, Pieter Abbeel, Josep M. Porta, Ken Golberg. Presented at IROS 2011

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The Berkeley PR2 folding a towel via the g-fold method. Details available at rll.berkeley.edu

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