A Mixture of Experts Approach to 3D Human Motion Prediction

This project addresses the challenge of human motion prediction, a critical area for applications such as au- tonomous vehicle movement detection. Previous works have emphasized the need for low inference times to provide real time performance for applications like these. Our primary objective is to critically evaluate existing model ar- chitectures, identifying their advantages and opportunities for improvement by replicating the state-of-the-art (SOTA) Spatio-Temporal Transformer model as best as possible given computational con- straints. These models have surpassed the limitations of RNN-based models and have demonstrated the ability to generate plausible motion sequences over both short and long term horizons through the use of spatio-temporal rep- resentations. We also propose a novel architecture to ad- dress challenges of real time inference speed by incorpo- rating a Mixture of Experts (MoE) block within the Spatial- Temporal (ST) attention layer. The particular variation that is used is Soft MoE, a fully-differentiable sparse Transformer that has shown promising ability to enable larger model capacity at lower inference cost. We make out code publicly available at https://github.com/edshieh/motionprediction

Hawkeye: Hectometer-range Subcentimeter Localization for Large-scale mmWave Backscatter

Accurate localization of a large number of objects over a wide area is one of the keys to the pervasive interaction with the Internet of Things. This paper presents Hawkeye, a new mmWave backscatter that, for the first time, offers over (i) hundred-scale simultaneous 3D localization at (ii) subcentimeter accuracy for over an (iii) hectometer distance. Hawkeye generally applies to indoors and outdoors as well as under mobility. Hawkeye tag's Van Atta Array design with retro-reflectivity in both elevation and azimuth planes offers 3D localization and effectively suppresses the multipath. Hawkeye localization algorithm is a lightweight signal processing compatible with the commodity FMCW radar. It uniquely leverages the interplay between the tag signal and clutter, and leverages the spetral leakage for fine-grained positioning. Prototype evaluations in corridor, lecture room, and soccer field reveal 6.7 mm median accuracy at 160 m range, and simultaneously localizes 100 tags in only 33.2 ms. Hawkeye is reliable under temperature change with significant oscillator frequency offset.