Spaces:
Running
Running
| title: DVIDIA Training | |
| colorFrom: gray | |
| colorTo: blue | |
| sdk: static | |
| app_file: index.html | |
| license: mit | |
| tags: | |
| - robotics | |
| - simulation | |
| - offline | |
| - cpu | |
| - research | |
| # DVIDIA Training | |
| A research alpha toward installable robot skills. This static Space presents published evidence from the local DVIDIA Training CPU pipeline: a synthetic visual baseline, an optional learned joint-delta candidate trained from aligned native simulation labels, and one held-out scene's recorded contact/lift/place qualification with an open-jaw control. | |
| **Training runs on your own computer or server.** This Space provides no hosted training or uploads. The replay shows saved native physics states; it does not execute a new physics experiment. | |
| ## Published evidence | |
| - [Example dataset](https://huggingface.co/datasets/Dvidia/dvidia-training-examples): explicitly synthetic fixtures and native simulation evidence. | |
| - [Pilot artifacts](https://huggingface.co/Dvidia/dvidia-training-pilot): learned weights, evaluation reports, and qualification/control evidence. | |
| - [Local training source and installation](https://github.com/Dvidia-Inference/dvidia-training). | |
| - [Methods and measured limitations](https://research.dvidia.org/papers/skillspace-footage-training/). | |
| The visual benchmark uses ten six-second synthetic moving-disc clips, with seven training, two development, and one test group. Three pipeline repeats on one Apple M5 macOS host have a median wall time of 0.5106 seconds. The same held-out recording is re-scored; these are throughput and visual prediction measurements, not independent robotics success trials or minimum hardware requirements. | |
| The movement head is trained from 4,029 aligned numerical labels across seven native simulation recordings and evaluated on one held-out recording. The installed candidate passes native qualification in that recording's exact scene; identical joint targets with open jaws fail. Its authored phase, gripper/contact, and recovery supervisor remains in use, with privileged simulator observations and no camera-based control. | |
| ## Scope | |
| Visual learning is not an acquired robot skill. No human-video action bridge, shoe organization policy, general scene capability, demonstration sufficiency threshold, or qualified hardware skill is established. Hashes check artifact consistency, not capture authenticity or independently measured truth. | |
| This page is self-contained apart from its staged local banner at `assets/branding/github-header.png` and standalone recorded replay at `replay.html`. It loads no third-party scripts, fonts, or CDNs, and contains no server or credential. Source code and published synthetic artifacts use the MIT license; see the source repository and artifact cards for scope and provenance. | |