To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects.

In this work, we argue that the order of play in strategic classification is fundamentally determined by the relative frequencies at which the decision-maker and the agents adapt to each other's actions.

Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation.

This appendix is organized as follows. In Section A (referred by CheckList), we discuss the societal impact. In Section B.1 (referred by Section 4.1), we summarize the statistics of the datasets used in In Section B.2 (referred by Section 4), we introduce the pre-training and model adaptation In Section B.3, we provide zero-shot retrieval comparison by introducing knowledge. In Section B.4, we provide quantitative analysis on how external knowledge benefit transfer. In Section B.5 (referred by Section 4.2 and 4.3), we provide more visualizations of success In Section B.6, we provide more object detection results by training on larger dataset for We do not anticipate a specific negative impact, but, as with any Machine Learning method, we recommend to exercise caution.