  When working alongside human collaborators in dynamic and unstructured
environments, such as disaster recovery or military operation, fast field
adaptation is necessary for an unmanned ground vehicle (UGV) to perform its
duties or learn novel tasks. In these scenarios, personnel and equipment are
constrained, making training with minimal human supervision a desirable
learning attribute. We address the problem of making UGVs more reliable and
adaptable teammates with a novel framework that uses visual perception and
inverse optimal control to learn traversal costs for environment features.
Through extensive evaluation in a real-world environment, we show that our
framework requires few human demonstrated trajectory exemplars to learn feature
costs that reliably encode several different traversal behaviors. Additionally,
we present an on-line version of the framework that allows a human teammate to
intervene during live operation to correct deteriorated behavior or to adapt
behavior to dynamic changes in complex and unstructured environments.
