#Robot models and learning

Why it matters: The original article provides scale figures for the Index data pipeline and a five-stage processing workflow, which readers can use to assess the actual progress of its human data collection approach.

Why it matters: The original article provides comparison figures for ICL and VLA at the same data scale and a deployment timeline, allowing readers to assess the feasibility of the in-context learning approach for manipulation tasks.

Why it matters: The research team released experimental results on navigation of magnetically controlled nanorobot swarms in non-human primates. Readers interested in the medical translation of micro- and nanorobots can learn about its technical approach.

Why it matters: The original text provides actual performance figures such as line takt time, yield, and deployment period, allowing readers to understand the implementation path of the embodied dual-arm solution in a mass-production workshop.

Why it matters: The model brings touch into large-scale pretraining along with a self-developed pseudo-labeling system, letting readers understand the path by which tactile data goes from a lab signal to a scalable pretraining resource.

Why it matters: The original text provides verifiable details such as FlashSAC's training speed and validation scope, letting readers understand how reinforcement learning algorithms can shorten the humanoid robot policy development cycle.

Why it matters: The official disclosure provides detailed quantitative data on pretraining scale and generalization gaps, allowing readers to understand how a small amount of demonstration data transfers to unseen environments.

Why it matters: The original article explains the sources of Muon's overhead in distributed training and three improvement approaches, which embodied model teams can use to assess the feasibility of improving training efficiency.

Why it matters: The original text explains the architectural composition of V³-0 and its human-in-the-loop real-robot reinforcement learning loop, which lets readers understand where the mobile whole-body manipulation capability comes from.

Why it matters: The original text presents a complete chain from human-hand data processing and real-robot correction to model architecture and experimental figures, allowing readers to assess its reproducibility value.

Why it matters: It provides a same-benchmark success-rate comparison with π0.5 and single-GPU real-time inference metrics, helping to gauge where this kind of world-action model stands in terms of performance.

Why it matters: The material provides comparison scores on RBench and the scope of the open-source release, helping readers judge the starting point and usability of this embodied video model.

Why it matters: The original work proposes a method for adapting a frozen world-action model at test time using only unlabeled human videos, without robot demonstrations or fine-tuning, offering methodological reference value for researchers who want to reuse it.

Why it matters: The public weights and GM-100 results let readers directly compare its gap with π0.5 and GR00T N1.7 on long-horizon mobile manipulation.

Why it matters: The report details how all four financing rounds were fully closed and the deployment scenarios, giving readers a sense of how substantive the company's financing progress really is.

Why it matters: The original text presents X-Tokenizer's method design and comparative experimental data, letting readers understand the action tokenizer's multimodal alignment approach and its reusable effects.