Combining coarse and fine physics for manipulation using parallel-in-time integration
We present a method for fast and accurate physics-based predictions during non-prehensile
manipulation planning and control. Given an initial state and a sequence of controls, the
problem of predicting the resulting sequence of states is a key component of a variety of
model-based planning and control algorithms. We propose combining a coarse (ie
computationally cheap but not very accurate) predictive physics model, with a fine (ie
computationally expensive but accurate) predictive physics model, to generate a hybrid …
manipulation planning and control. Given an initial state and a sequence of controls, the
problem of predicting the resulting sequence of states is a key component of a variety of
model-based planning and control algorithms. We propose combining a coarse (ie
computationally cheap but not very accurate) predictive physics model, with a fine (ie
computationally expensive but accurate) predictive physics model, to generate a hybrid …
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