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Motion forecasting plays a significant role in various domains (e.g.,autonomous driving, human-robot interaction), which aims to predict futuremotion sequences given a set of historical observations. However, the observedelements may be of different levels of importance. Some information may beirrelevant or even distracting to the forecasting in certain situations. Toaddress this issue, we propose a generic motion forecasting framework (namedRAIN) with dynamic key information selection and ranking based on a hybridattention mechanism. The general framework is instantiated to handlemulti-agent trajectory prediction and human motion forecasting tasks,respectively. In the former task, the model learns to recognize the relationsbetween agents with a graph representation and to determine their relativesignificance. In the latter task, the model learns to capture the temporalproximity and dependency in long-term human motions. We also propose aneffective double-stage training pipeline with an alternating training strategyto optimize the parameters in different modules of the framework. We validatethe framework on both synthetic simulations and motion forecasting benchmarksin different domains, demonstrating that our method not only achievesstate-of-the-art forecasting performance, but also provides interpretable andreasonable hybrid attention weights.

RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting