lfd: Learning from Demonstrations for Robotic Manipulation¶
lfd is a software framework for generalizing robot trajectories from demonstrations into new situations, thus enabling robots to learn to perform tasks from demonstrations.
Source code is available on github.
|[ISRR2013]||John Schulman, Jonathan Ho, Cameron Lee, Pieter Abbeel, “Learning from Demonstrations through the Use of Non-Rigid Registration,” in Proceedings of the 16th International Symposium on Robotics Research (ISRR), 2013.|
|[IROS2013]||John Schulman, Ankush Gupta, Sibi Venkatesan, Mallory Tayson-Frederick, Pieter Abbeel, “A Case Study of Trajectory Transfer through Non-Rigid Registration for a Simplified Suturing Scenario,” in Proceedings of the 26th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013.|
|[IROS2014]||Alex X. Lee, Sandy H. Huang, Dylan Hadfield-Menell, Eric Tzeng, Pieter Abbeel, “Unifying Scene Registration and Trajectory Optimization for Learning from Demonstrations with Application to Manipulation of Deformable Objects,” in Proceedings of the 27th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014.|