Physical grounding

Live PiPER shoe workstation — 13 October 2026

Live PiPER shoe workstation v0.1 adds browser-local MuJoCo physics, a fixed-orientation target controller, native jaw commands and a free rigid trainer on a physical worktop. Users can move a target, inspect tracking and contact, nudge the shoe and download session diagnostics. The run archive keeps frozen benchmarks, exploratory failures and native/browser comparisons separate. All fifteen development retained-lift attempts failed. Shoe properties remain untested assumptions; no learned shoe policy or hardware qualification is established. Open the live workstation.

Robotics research pipeline — 9 October 2026

Robotics research pipeline connects footage review, synchronized measured state, separate visual and movement training, frozen simulation evaluation and versioned skill installation. Its diagram identifies current simulation components, proposed automatic labeling and learned task decisions, and the outstanding physical qualification gate. The proposed supervisor consumes recent joint, gripper and contact evidence; it does not equate a close command with a successful grasp. The new annotation-labor pilot is a companion to the existing graduation proposal. Both new experiments are not run. Existing recorded benchmarks retain their original protocols and claims.

Skillspace footage training pipeline introduces the separate public DVIDIA Training repository: CPU footage intake, connected source groups, measured visual fitting, optional aligned movement training and simulation capsule export. Hardware guidance, installation tools and synthetic examples are included. One exact native scene passes with a failed open-jaw control; human-video action recovery and shoe competence remain unestablished. Release and evidence.

Installable learned movement capsule — 7 October 2026

Skill capsule acquisition and optimization packages a learned CPU movement head inside an authored placement supervisor. A new frozen evaluation preserves teacher/student 7/8 outcomes, with no overall speedup. The local lab imports the JSON capsule, qualifies the exact scene with native placement and an identical-command/open-jaw control, and runs without retraining. Changed inputs require another test. Source and evidence, capsule, browser-run replay. The capsule does not supply video learning or physical qualification.

Paired arm benchmark and Skillspace evolution — 7 October 2026

Skillspace arm benchmarks and evolution publishes two development revisions and their frozen comparison. Development completion rises from 14/20 to 17/20, while held-out completion falls from 30/32 to 29/32: nominal remains 24/24 and actuator stress falls from 6/8 to 5/8. Observed instrumented CPU throughput falls from 12.28× to 11.39×. These results support explicit inputs, feedback and failure reporting; they do not establish better overall completion or calibrated physics. Protocol and measurements, source release, browser-run replay. The proposed release inspector keeps draft simulation/hardware readiness false; future tasks require their own evidence.

Skillspace link to articulated arm — 7 October 2026

Installing a Skillspace on a simulated arm publishes a one-click local installer, authored placement adapter and native articulated-arm environment. The same controller completes 6/6 changed layouts; no-motion and replayed joint commands with jaws held open each complete 0/6. Three layouts repeat successfully under native network denial. Local product guide and source. DVIDIA metadata provides task identity, not a learned policy; the box proxy, privileged state and collision-excluded arm links bound the result.

Runnable cable environment — 7 October 2026

Offline cable training environment publishes the first practice-to-pack prototype: native CPU cable dynamics, local controller search, frozen simulated evaluation, replay and hashed export. The selected controller and fixed feedback baseline both completed 10/10 held-out episodes; zero-force and release controls completed 0/10. Search did not improve held-out completion. A separate native macOS network-denial check completed 3/3 additional episodes. Runnable archive and instructions. Ideal endpoint attachment and privileged state remain explicit; this is not the retrieval baseline, physical gripping, calibrated rope accuracy or GPU qualification.

Recorded contact fixture — 7 October 2026

The separate native CPU contact fixture records 54 deterministic trials across 18 cases and three repeats. The decision report explains why endpoint agreement does not establish fingertip-force fidelity, and why coarse timesteps can miss collisions. Benchmark source, pinned dependencies, per-trial JSON, an independent AI-assisted code review and a bounded Python-network denial check are published. This fixture does not execute the retrieval baseline below or validate rope materials, GPU training or physical robot transfer.

Question: Which observed states, contact signals or controlled synthetic examples improve a defined task beyond coarse video tags?

Status: scoping · Updated 6 October 2026 · Results: not run

Open a research question · Baseline protocol · Bibliography

Primary sources

• trex2026paper — Dantong Niu et al., T-Rex: Tactile-Reactive Dexterous Manipulation, 15 June 2026.
• trex2026data — T-Rex public dataset card, 2026. Subset size, modalities and preprocessing provenance.
• gr00t2025n1 — NVIDIA (Johan Bjorck et al.), GR00T N1: An Open Foundation Model for Generalist Humanoid Robots, 18 March 2025.
• nvidia2025motion — NVIDIA, Building a Synthetic Motion Generation Pipeline for Humanoid Robot Learning, 18 March 2025.
• nvidia2026blueprint — NVIDIA's synthetic manipulation blueprint, repository accessed 2026; this year is an access date.

What is known and unknown

T-Rex reports 100 collected hours of tactile robot data; its public dataset is approximately 50 hours, not the full training mixture. The card includes RGB, tactile/deformation streams, joints/actions and wrench measurements, and records preprocessing such as imputation trex2026paper, trex2026data. The dataset card carries an MIT license tag; pin the revision and audit the actual artifacts before reuse. These terms do not grant rights to unrelated human videos.

NVIDIA's 11-hour simulation generation and GR00T N1's neural-video generation GPU-hours describe different processes. The vendor article's 40% gain has an unresolved denominator in this review; it is not a DVIDIA result nvidia2025motion, gr00t2025n1. The blueprint's 48 GB A6000 and optional separate 80 GB H100-class workflow are documented workflow requirements, not a universal minimum nvidia2026blueprint.

Unknown: whether richer metadata improves held-out-object retrieval, whether annotation cost is worthwhile, and whether a synthetic intervention transfers outside its controlled setting. This topic starts with retrieval; physical execution requires its own robot evaluation.

Open questions

1. pg-01 — Do temporal state descriptions and measured contact descriptors improve retrieval across held-out objects?
2. pg-02 — Which failures require physical sensing rather than additional RGB labels?
3. pg-03 — Does controlled synthetic augmentation improve held-out simulated completion under equal training budgets?

First small experiment

Audit a capped public T-Rex subset for state-aware retrieval. Inspect metadata first, then double-annotate a small eligible subset. Compare coarse tags with temporal preconditions/effects and, separately, measured contact descriptors. Aim for up to 60 episodes across six primitives only if real coverage and a 2 GB download cap permit it.

Measure Recall@5, state compatibility, disagreement, annotation minutes, decoded bytes, runtime and peak memory. The proposed continuation threshold is a ten-percentage-point Recall@5 gain without lower state compatibility. Use CPU and a fixed simple retrieval method first. Stop if rights, sensor provenance or object-disjoint evaluation are unresolved, or if the cap is reached; reduce the sample transparently rather than downloading the whole dataset.

A later simulator experiment must compare fixed real/teleoperated demonstrations with the same set plus generated trajectories under equal updates and held-out scene seeds. Keep strict completion distinct from partial progress, and appearance generation separately costed. This follow-on is not authorized or run by the baseline.

Milestones

• Primary sources reviewed
• Public-subset and annotation pilot reviewed
• Retrieval baseline executed and artifacts published
• Independent review before simulator expansion

Results

Not run. No data acquisition, annotation, training, robot execution or synthetic-generation result is claimed. Published vendor/paper figures remain attributed to their original settings.