  One unresolved issue is how to scale model-based inverse reinforcement
learning (IRL) to actual robotic manipulation tasks with unpredictable
dynamics. The ability to learn from both visual and proprioceptive examples,
creating algorithms that scale to high-dimensional state-spaces, and mastering
strong dynamics models are the main obstacles. In this work, we provide a
gradient-based inverse reinforcement learning framework that learns cost
functions purely from visual human demonstrations. The shown behavior and the
trajectory is then optimized using TD visual model predictive control(MPC) and
the learned cost functions. We test our system using fundamental object
manipulation tasks on hardware.
