Robotics
PyTorch
Cosmos
xperience10m_task_baseline_suite
embodied-ai
multimodal
xperience-10m
baseline
evaluation
qwen3-omni
Instructions to use cy0307/ropedia-xperience-10m-task-baselines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use cy0307/ropedia-xperience-10m-task-baselines with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Single-Episode Diagnostics Index
These outputs are local diagnostics built from the existing one-episode Xperience-10M artifacts. They are designed for manual verification while waiting for full multi-episode data access.
Generated Analyses
modality_ablation/: compact ridge-head ablations across real feature blocks.timeline_overlay/: existing prediction CSVs aligned to the episode timeline.alignment_stress/: cross-modal retrieval under explicit time-shift perturbations.provenance.json: input hashes, feature dimensions, and source artifact identifiers.
Validity Boundaries
- This is a single-episode diagnostic, not a full Xperience-10M benchmark.
- Rows marked
not_computedare intentionally left blank when train labels or valid splits are unavailable. - Rows marked
derived_perturbationuse real features with deliberate time shifts for stress testing.
Counts
- Ablation rows: 108; computed: 108.
- Timeline overlay rows: 2079.
- Alignment stress rows: 54.
- Shared feature shape: 1161 windows x 8546 features.