Blog
About

5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      LVIS: Learning from Value Function Intervals for Contact-Aware Robot Controllers

      Preprint

      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Guided policy search is a popular approach for training controllers for high-dimensional systems, but it has a number of pitfalls. Non-convex trajectory optimization has local minima, and non-uniqueness in the optimal policy itself can mean that independently-optimized samples do not describe a coherent policy from which to train. We introduce LVIS, which circumvents the issue of local minima through global mixed-integer optimization and the issue of non-uniqueness through learning the optimal value function (or cost-to-go) rather than the optimal policy. To avoid the expense of solving the mixed-integer programs to full global optimality, we instead solve them only partially, extracting intervals containing the true cost-to-go from early termination of the branch-and-bound algorithm. These interval samples are used to weakly supervise the training of a neural net which approximates the true cost-to-go. Online, we use that learned cost-to-go as the terminal cost of a one-step model-predictive controller, which we solve via a small mixed-integer optimization. We demonstrate the LVIS approach on a cart-pole system with walls and a planar humanoid robot model and show that it can be applied to a fundamentally hard problem in feedback control--control through contact.

          Related collections

          Most cited references 3

          • Record: found
          • Abstract: not found
          • Article: not found

          A direct method for trajectory optimization of rigid bodies through contact

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Combining the benefits of function approximation and trajectory optimization

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Balancing and Walking Using Full Dynamics LQR Control With Contact Constraints

              Torque control algorithms which consider robot dynamics and contact constraints are important for creating dynamic behaviors for humanoids. As computational power increases, algorithms tend to also increase in complexity. However, it is not clear how much complexity is really required to create controllers which exhibit good performance. In this paper, we study the capabilities of a simple approach based on contact consistent LQR controllers designed around key poses to control various tasks on a humanoid robot. We present extensive experimental results on a hydraulic, torque controlled humanoid performing balancing and stepping tasks. This feedback control approach captures the necessary synergies between the DoFs of the robot to guarantee good control performance. We show that for the considered tasks, it is only necessary to re-linearize the dynamics of the robot at different contact configurations and that increasing the number of LQR controllers along desired trajectories does not improve performance. Our result suggest that very simple controllers can yield good performance competitive with current state of the art, but more complex, optimization-based whole-body controllers. A video of the experiments can be found at https://youtu.be/5T08CNKV1hw.
                Bookmark

                Author and article information

                Journal
                15 September 2018
                Article
                1809.05802

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                7 pages, 8 figures. Submitted to the 2019 IEEE International Conference on Robotics and Automation (ICRA 2019)
                cs.RO

                Robotics

                Comments

                Comment on this article