Efficient Real-Time Inference in Temporal Convolution Networks
It has been recently demonstrated that Temporal Convolution Networks (TCNs) provide state-of-the-art results in many problem domains where the input data is a time-series. TCNs typically incorporate information from a long history of inputs (the receptive field) into a single output using many convolution layers. Real-time inference using a trained TCN can be challenging on devices with limited compute and memory, especially if the receptive field is large. This paper introduces the RT-TCN algorithm that reuses the output of prior convolution operations to minimize the computational requirements and persistent memory footprint of a TCN during real-time inference. We also show that when a TCN is trained using time slices of the input time-series, it can be executed in real-time continually using RT-TCN. In addition, we provide TCN architecture guidelines that ensure that real-time inference can be performed within memory and computational constraints.
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
Deep reinforcement learning (RL) has broughtmany successes for autonomous robot navigation. However,there still exists important limitations that prevent real-worlduse of RL-based navigation systems. For example, most learningapproaches lack safety guarantees; and learned na…
Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways
While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other i…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to “real world” events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and syst…
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