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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