#Data and simulation

Why it matters: The original provides a controlled experiment showing that feeding raw tactile signals into the action model instead lowers the success rate, along with the improvement after unifying tactile tokens, which can serve as a reference for tactile representation design.

Why it matters: GR00T 2's few-shot success rates and Isaac Lab 3.0's training throughput gains provide the current baseline for embodied models and the simulation training stack.

Why it matters: NVIDIA released the GR00T 2 series at CoRL 2026 and disclosed the scale of three types of pre-training data along with success rates for cross-embodiment transfer and few-shot adaptation, which can be used to assess the current capability boundaries of world-action models.

Why it matters: The original article provides quantitative comparisons of the whole-body control model's real-machine error and teleoperation collection efficiency, which can be used to assess how the cerebellum layer's precision affects the success rate of upper-layer VLA tasks.

Why it matters: The technical report uses controlled experiments to break down the contributions of human data, action supervision, and embodiment diversity to dexterous manipulation, which can inform judgments about data-mix strategies.

Why it matters: The original text gives the success-rate changes for four groups of household tasks from round 0 to round 30 updates, showing the practical benefits of the self-evolving training loop.

Why it matters: Readers can see a unified world-action model approach that does not rely on large-scale robot pretraining, along with the specific design of a shared backbone for navigation and manipulation.

Why it matters: Readers can learn how a publicly accessible embodied API hands zero-shot generalization capability over to external developers for testing, as well as its success-rate data from its own evaluation.

Why it matters: The original text provides the two-stage training recipe and results across multiple leaderboards, which readers can use to judge the feasibility of adapting video generation models into simulators.

Why it matters: The original text provides real-robot success rate comparisons between E0.5 and π0.5 on dual-arm and humanoid platforms, which can be used to assess the manipulation generalization performance of the 3D geometric backbone approach.

Why it matters: AgiBot links pretraining, post-training, and post-deployment learning into a single pipeline, which readers can use to compare differences in scaling approaches for embodied models.

Why it matters: AgiBot disclosed GO-3 Preview's three-stage learning pipeline of pretraining, post-training, and post-deployment, along with specific benefit data from mixed training using real-machine clusters and world models.

Why it matters: The original article fully discloses GO-3 Preview's three-stage design of pretraining, post-training, and post-deployment learning, which can be used to understand the engineering path for scaling embodied models across their full lifecycle.

Why it matters: Two open-source datasets entering the top five of the Hugging Face robotics dataset trending list can serve as a reference for observing the level of attention to whole-body manipulation data.

Why it matters: Genie 4 combines real-time world simulation, native audio, and multi-step reasoning, which lets readers judge that robot training environments are shifting from manual modeling to generative simulation.

Why it matters: The materials present a multimodal unified representation architecture and 19-category disciplinary evaluation benchmarks, offering insight into how materials large models can move from retrieval to mechanistic reasoning.

Why it matters: The original shows live demonstrations of the world model on two types of tasks—a robotic arm opening a can and a humanoid playing table tennis—which can be used to understand its approach to generalization across tasks and embodiments.

Why it matters: The original text presents the component design and data-scaling curves for pretraining a world-action model from scratch, allowing observation of changes in zero-shot cross-embodiment transfer.

Why it matters: The original lists Cosmos's new training tools, simulation checkpoints and data blueprints, and gives specific data on users such as Porsche and ANYbotics, which can be used to assess changes in the world model development workflow.

Why it matters: NVIDIA's open-source data engine cuts robot video labeling costs to about 1/125 of a general-purpose large model and provides several benchmark comparisons, which can be used to assess the substitution space in the data labeling segment.