Model Based Inverse Reinforcement Learning from Visual Demonstrations

Goal: Learn Manipulation from Visual Human Demos

Paper Abstract

Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem. The key challenges lie in learning good dynamics models, developing algorithms that scale to high-dimensional state-spaces and being able to learn from both visual and proprioceptive demonstrations. In this work, we present a gradient-based inverse reinforcement learning framework that utilizes a pre-trained visual dynamics model to learn cost functions when given only visual human demonstrations. The learned cost functions are then used to reproduce the demonstrated behavior via visual model predictive control. We evaluate our framework on hardware on two basic object manipulation tasks.

Details

Click to know more about:

• Visual MPC Framework

• Learning a Cost Function

• Experiments

Links

• Paper Link: https://arxiv.org/abs/2010.09034

• Code: Our code is a part of the LearningToLearn repository @ FacebookResearch and can be found in https://github.com/facebookresearch/LearningToLearn/tree/main/mbirl

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