Model-Based Inverse Reinforcement Learning from Visual Demonstrations

• URL: http://arxiv.org/abs/2010.09034v2
• Date: Wed, 6 Jan 2021 19:12:17 GMT
• Title: Model-Based Inverse Reinforcement Learning from Visual Demonstrations
• Authors: Neha Das and Sarah Bechtle and Todor Davchev and Dinesh Jayaraman and Akshara Rai and Franziska Meier
• Abstract summary: We present a gradient-based inverse reinforcement learning framework that learns 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.
• Score: 20.23223474119314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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