Visual Foresight With a Local Dynamics Model
- URL: http://arxiv.org/abs/2206.14802v1
- Date: Wed, 29 Jun 2022 17:58:14 GMT
- Title: Visual Foresight With a Local Dynamics Model
- Authors: Colin Kohler, Robert Platt
- Abstract summary: We propose the Local Dynamics Model (LDM) which efficiently learns the state-transition function for single-step manipulation primitives.
By combining the LDM with model-free policy learning, we can learn policies which can solve complex manipulation tasks using one-step lookahead planning.
- Score: 1.370633147306388
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Model-free policy learning has been shown to be capable of learning
manipulation policies which can solve long-time horizon tasks using single-step
manipulation primitives. However, training these policies is a time-consuming
process requiring large amounts of data. We propose the Local Dynamics Model
(LDM) which efficiently learns the state-transition function for these
manipulation primitives. By combining the LDM with model-free policy learning,
we can learn policies which can solve complex manipulation tasks using one-step
lookahead planning. We show that the LDM is both more sample-efficient and
outperforms other model architectures. When combined with planning, we can
outperform other model-based and model-free policies on several challenging
manipulation tasks in simulation.
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