Infinite-Horizon Model Predictive Control for Periodic Tasks with Contacts. Erez, Tassa, Todorov. RSS 2011

I think this was already covered when I read Erez’ thesis, but it was on my kindle, so here goes

  1. Combines offline trajectory optimization and online model predictive control (MPC)
  2. The method for the hopper domain
  3. Use offline optimization to find the limit cycle for the task, and then compute a locally quadratic estimation of the value around the limit cycle. This quadratic estimation is used as the terminal cost for online MPC
  4. The approach Infinite-horizon Model Predictive Control
    1. Was previously only applied to small domains, first time applied to periodic domains
  5. They claim online optimization is ineffective in domains with contacts because of myopic behavior stemming from the limited horizon
    1. I remember reading this in the thesis, it is an artifact of the approach they use and not a fundamental limitation
    2. May have to do with the fact that they plan from the end of the sequence to the front?
  6. Infinite horizon planning can be used in domains with terminal states or limit cycles
  7. Planning falls into 2 categories:
    1. Simultaneous methods explicitly represent the trajectory in state space, treating the dynamics as constraints
    2. Sequential methods represent only the control sequence and use integration to evaluate the trajectory
  8. Apparently shooting algorithms attempt to solve BVP problems?
  9. They use the simultaneous (state-based policy) to plan the limit cycle offline, and sequential planning for the online part
  10. They build a model that uses inverse dynamics
    1. They need that so that they can just define the trajectory through the state-space desired and then derive the actions from that

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