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 CLAMP: Combined Learning from Demonstration and Motion Planning
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-CLAMP is an probabilistic inference based approach which learns a a trajectory prior from human demonstrations and uses factor graph optimization for efficient skill reproduction. 
+CLAMP is an probabilistic inference based approach which learns a a trajectory prior from human demonstrations and uses factor graph optimization for efficient skill reproduction. The baseline capabilities of CLAMP are described in [Towards robust skill generalization: Unifying learning from demonstration and motion planning](http://proceedings.mlr.press/v78/rana17a/rana17a.pdf) (CoRL 2017). Extensions of CLAMP to learn from demonstrations provided in clutter as well as incrementally updating the trajectory prior are provided in [Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments](https://arxiv.org/pdf/1808.00349.pdf) (IROS 2018). 
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+This library is an implementation of CLAMP. The core library is developed in C++ language, alongside a MATLAB frontend. Demo scripts are provided in MATLAB to reproduce some of the results in our publications.
 
 CLAMP is being developed by [M. Asif Rana](mailto:[email protected]) and [Mustafa Mukadam](mailto:[email protected]) at the Georgia Tech Institute for Robotics & Intelligent Machines.