  In contact-rich tasks, the hybrid, multi-modal nature of contact dynamics
poses great challenges in model representation, planning, and control. Recent
efforts have attempted to address these challenges via data-driven methods,
learning dynamical models in combination with model predictive control. Those
methods, while effective, rely solely on minimizing forward prediction errors
to hope for better task performance with MPC controllers. This weak correlation
can result in data inefficiency as well as limitations to overall performance.
In response, we propose a novel strategy: using a policy gradient algorithm to
find a simplified dynamics model that explicitly maximizes task performance.
Specifically, we parameterize the stochastic policy as the perturbed output of
the MPC controller, thus, the learned model representation can directly
associate with the policy or task performance. We apply the proposed method to
contact-rich tasks where a three-fingered robotic hand manipulates previously
unknown objects. Our method significantly enhances task success rate by up to
15% in manipulating diverse objects compared to the existing method while
sustaining data efficiency. Our method can solve some tasks with success rates
of 70% or higher using under 30 minutes of data. All videos and codes are
available at https://sites.google.com/view/lcs-rl.
