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27 April 2024

» arxiv » 2209.10780

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Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Xuesu Xiao ; Tingnan Zhang ; Krzysztof Choromanski ; Edward Lee ; Anthony Francis ; Jake Varley ; Stephen Tu ; Sumeet Singh ; Peng Xu ; Fei Xia ; Sven Mikael Persson ; Dmitry Kalashnikov ; Leila Takayama ; Roy Frostig ; Jie Tan ; Carolina Parada ; Vikas Sindhwani ;
Date:	22 Sep 2022
Abstract:	Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e.g., in cluttered home environments or in human-occupied public spaces. To address this, we present a new class of implicit control policies combining the benefits of imitation learning with the robust handling of system constraints from Model Predictive Control (MPC). Our approach, called Performer-MPC, uses a learned cost function parameterized by vision context embeddings provided by Performers -- a low-rank implicit-attention Transformer. We jointly train the cost function and construct the controller relying on it, effectively solving end-to-end the corresponding bi-level optimization problem. We show that the resulting policy improves standard MPC performance by leveraging a few expert demonstrations of the desired navigation behavior in different challenging real-world scenarios. Compared with a standard MPC policy, Performer-MPC achieves >40% better goal reached in cluttered environments and >65% better on social metrics when navigating around humans.
Source:	arXiv, 2209.10780
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