MODELING TASK UNCERTAINTY FOR SAFE META-IMITATION LEARNING

Modeling Task Uncertainty for Safe Meta-Imitation Learning

To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach.In particular, some recent meta-learning methods are shown to solve novel tasks by leveraging their experience of performing other tasks during training.Although studies ar

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Spatial–Temporal Attention-Based Human Dynamics Retrospection

Motivated by impressive success of deep recurrent neural networks (RNNs), sequence-to-sequence (seq2seq) architecture has been widely adapted to tackle human motion prediction.However, forecasting in longer time horizons always leads to implausible human poses or converges to mean poses.To address these challenges, we dig into the root causes and l

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