The Constraint Consistent Learning (CCL) Library implements a family of data-driven methods that are capable of (i) learning state-independent and -dependent constraints, (ii) decomposing the behaviour of redundant systems into task- and null-space parts, and (iii) uncovering the underlying null space control policy.

We have provided three demos to demonstrate the toolbox:

1. demo_toy_example_2D.m
2. demo_2_link_arm.m
3. demo_with_deal_data.m

For details of the methodologies, please refer to:

Towell, M. Howard, and S. Vijayakumar. IEEE International Conference Intelligent Robots and Systems, 2010. H.-C. Lin, M. Howard, and S. Vijayakumar. IEEE International Conference Robotics and Automation, 2015 Howard, Matthew, et al. "A novel method for learning policies from variable constraint data." Autonomous Robots 27.2 (2009): 105-121.