China's humanoid robots aren't smart enough to take your job -- yet A human trainer teaches a robot how to package noodles while holding a controller and wearing a headset fitted with motion sensors at a robot data training center in Liuzhou, Guangxi, China. LIUZHOU/BEIJING - Inside a training center in southern China, more than 100 humanoid robots stand in neat rows. The humanoids are slow and awkward. A novice trainer might get them to produce one usable movement in 300 attempts; an experienced trainer, one in 50. Robot maker UBTech won an $18 million tender from the Guangxi regional government last October to supply the training facility with humanoids and related hardware. The mission: to produce training data for so-called embodied AI -- robots able to perceive, decide and act in the physical world.

Obtaining the human-like perception ability of abstracting visual concepts from concrete pixels has always been a fundamental and important target in machine learning research fields such as disentangled representation learning and scene decomposition. Towards this goal, we propose an unsupervised transformer-based Visual Concepts Tokenization framework, dubbed VCT, to perceive an image into a set of disentangled visual concept tokens, with each concept token responding to one type of independent visual concept. Particularly, to obtain these concept tokens, we only use cross-attention to extract visual information from the image tokens layer by layer without self-attention between concept tokens, preventing information leakage across concept tokens. We further propose a Concept Disentangling Loss to facilitate that different concept tokens represent independent visual concepts. The cross-attention and disentangling loss play the role of induction and mutual exclusion for the concept tokens, respectively. Extensive experiments on several popular datasets verify the effectiveness of VCT on the tasks of disentangled representation learning and scene decomposition. VCT achieves the state of the art results by a large margin.

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