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December 9, 2024
The rapid advancements in Artificial Intelligence, especially in large language models (LLMs), are opening up new possibilities for robotics. LLMs, trained on massive amounts of text, demonstrate impressive performance in various natural language processing tasks. This success raises hopes for robotics, which has long been limited by the high cost of action-labeled data. Could a similar approach of generative pre-training, applied to the abundance of interaction-rich video data, revolutionize robot learning?
The challenge lies in finding an effective representation for autoregressive pre-training that benefits robot manipulation tasks. Inspired by the way humans learn new skills by observing dynamic environments, the importance of motion-related knowledge comes to the forefront. This knowledge is closely linked to the fundamental actions of a robot and is hardware-independent, facilitating the transfer of learned movements to real robot actions.
Moto, a novel approach, addresses precisely this challenge. Moto converts video content into sequences of latent motion tokens using a Latent Motion Tokenizer. This creates a kind of "bridging language" of motion, learned from videos in an unsupervised manner. Through autoregressive prediction of the motion tokens, Moto-GPT, a GPT-based model, is pre-trained and thus captures diverse visual motion knowledge.
After pre-training, Moto-GPT demonstrates promising capabilities: It generates semantically interpretable motion tokens, predicts plausible motion trajectories, and evaluates the rationality of trajectories based on the output probability. To transfer the learned motion priors to real robot actions, a co-fine-tuning strategy is employed. This seamlessly connects the prediction of latent motion tokens with real robot control.
The Latent Motion Tokenizer operates with a VQ-VAE-based architecture. Two consecutive video frames are compressed into discrete tokens. By regularizing the decoder, which reconstructs the second image from the first image and the tokens, the tokenizer learns to capture the changes between the video frames, which are often caused by motion. The motion tokens obtained in this way are linked into a sequence that represents the motion trajectory.
Moto-GPT is subsequently trained to predict the next token based on the first image and the corresponding language instruction. For downstream robot manipulation tasks, action-query tokens are concatenated with the latent motion token chunk at each time step and jointly fine-tuned. The action-query tokens are processed by a learnable module to predict low-level actions, while the motion tokens are fine-tuned with the original next-token prediction mechanism.
Extensive experiments confirm the effectiveness of Moto. The latent motion tokens prove to be compact and meaningful representations of motion, effectively reconstructing and understanding motion trajectories in videos. The pre-trained Moto-GPT learns useful motion priors and predicts plausible motion trajectories. The fine-tuned Moto-GPT shows significant performance improvements over models without motion priors, especially with limited training data.
Moto opens up new avenues for robotics to leverage the knowledge contained in video data. The development of effective autoregressive representations for acquiring valuable priors through pre-training could significantly expand the capabilities of robots and lead to innovative applications in various fields. Mindverse, as a German company for AI-powered content creation, follows these developments with great interest and sees in approaches like Moto the potential to significantly shape the future of robotics. By developing customized AI solutions, such as chatbots, voicebots, and AI search engines, Mindverse contributes to bridging the gap between research and application and making the benefits of artificial intelligence available to businesses.
Bibliography: - https://arxiv.org/abs/2412.04445 - https://arxiv.org/html/2412.04445v1 - https://deeplearn.org/arxiv/555384/moto:-latent-motion-token-as-the-bridging-language-for-robot-manipulation - https://www.aimodels.fyi/authors/arxiv/Yuying%20Ge - https://simulately.wiki/daily/daily/ - https://www.researchgate.net/publication/370989372_Motion_Languages_for_Robot_Manipulation - https://ras.papercept.net/conferences/conferences/IROS24/program/IROS24_ContentListWeb_2.html - https://iros2024-abudhabi.org/accepted-papers - https://paperreading.club/category?cate=arXiv_AI - https://github.com/mbreuss/diffusion-literature-for-robotics