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arXiv · 1903.01385

Joint Perception and Control as Inference with an Object-based Implementation

Minne Li·Zheng Tian·Pranav Nashikkar·Ian Davies·Ying Wen·Jun Wang
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Abstract

Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a general framework to combine perception and control for partially observable environments through Bayesian inference. Based on the fact that object-level inductive biases are critical in human perceptual learning and reasoning, we propose Object-based Perception Control (OPC), an instantiation of PCI which manages to facilitate control using automatic discovered object-based representations. We develop an unsupervised end-to-end solution and analyze the convergence of the perception model update. Experiments in a high-dimensional pixel environment demonstrate the learning effectiveness of our object-based perception control approach. Specifically, we show that OPC achieves good perceptual grouping quality and outperforms several strong baselines in accumulated rewards.

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Minne Li, Zheng Tian, Pranav Nashikkar, Ian Davies, Ying Wen, Jun Wang. 2019-03-04. Joint Perception and Control as Inference with an Object-based Implementation. https://arxiv.org/abs/1903.01385

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