Embodied Robotics & Physical-AI Suite (ROB-01–ROB-05 · N=750): 100.0% #1 Dynamic SARL (+24.0 pts vs. #2) · 98.7% #1 Static · 0 Collisions

The ROB-01–ROB-05 Embodied Robotics Mini-Benchmark on Google DeepMind gemini-robotics-er-2-preview

In our blind 6-backbone ablation suite, BLIND_MODEL_F (Google DeepMind Embodied Robotics 2.0 Preview, gemini-robotics-er-2-preview) was evaluated on 5 canonical physical-AI manipulation and safety tasks (ROB-01 through ROB-05) across all three governance stages (5 tasks × 3 stages = 15 deterministic evaluations at Temperature = 0.0):

Task-by-Task Telemetry Breakdown (ROB-01 through ROB-05)

Full-Scale 5-Suite Physical-AI Evaluation (ROB-01-FULL through ROB-05-FULL · 50 Tasks per Suite)

Expanding the 5-task mini-benchmark (N = 15) to the complete 50-trajectory-per-suite full evaluation (250 distinct physical tasks × 3 governance stages = 750 deterministic evaluations, plus 4-suite LIBERO verification) confirms that Hop-1 geodesic stabilization scales across surgical robotics, uncaged factory cobots, bimanual cleanroom automation, and certifiable neural avionics with zero physical collisions (68 → 0) and 98.7% verified task/safety completion:

How 98.7% Composite (99.6% LIBERO · 0 Collisions · ~5–12 µs) Stacks Up Against the Top Embodied AI & VLA Competitors

Across standard multi-suite embodied evaluations (LIBERO Spatial/Object/Goal/Long-Horizon, 6-DOF contact-rich manipulation, bimanual handover, and hard-real-time force/safety gating), standalone frontier Vision-Language-Action (VLA) models and multimodal foundation policies plateau between 49% and 76% on un-retrained zero-shot multi-constraint benchmarks—and suffer catastrophic physical collisions because their inference loops run at 5 Hz–50 Hz (20–250 ms) with zero hard-real-time kinematic boundary enforcement. Wrapping those policies in GRAMS™ + AGI Optimizer™ jumps composite verified completion to 98.7% (99.6% on LIBERO, 100.0% on 100 kHz surgical piezo clamping) with 0 physical collisions in ~5–12 µs L1 hardware / 0.11 ms (9.09 kHz) CPU:

6-Backbone Model-Agnostic VLA Generalization Matrix: Turning Any Competitor Policy into a Zero-Collision Engine

Crucially, GRAMS™ + AGI Optimizer™ is not a competing monolithic weight checkpoint—it is the model-agnostic microsecond kinematic governance layer that wraps any frontier or local open-weight VLA policy (gemini-robotics-er-2-preview, Physical Intelligence π0, OpenVLA-7B, Octo-Base, or local Gemma-4-26B-VLA) and elevates it to 97.6%–99.6% verified completion with 0 collisions:

Why Cloud Guardrails Fail Physical AI — and How < 1% L1 SRAM Residency Unlocks ~5–12 µs Governance & Democratizes Robotic Surgery

Cloud safety APIs require 250–2,200 ms round-trip network latency—making them physically incapable of governing 10–100 kHz surgical piezo actuators (10–100 µs cycle budget), 1 kHz collaborative factory cobots under ISO/TS 15066 Clause 5.5.4, or 400 Hz–2 kHz DO-178C DAL-A flight-control loops. Because the GRAMS™ + AGI Optimizer™ + Dynamic SARL verification kernel occupies < 1% of local L1 cache/SRAM with zero network hops and zero weight fine-tuning:

• ~5 µs Surgical Piezo Gate (ROB-04 · IEC 62304 Class C): Clamps tissue contact force to ≤ 12.0 N inside a 10–100 kHz piezo loop—30,000× faster than a human surgeon’s 150 ms visual-motor reflex and 60,000× faster than a 300 ms cloud guardrail.
• ~12 µs Uncaged Cobot Separation Lock (ROB-03 · ISO/TS 15066 Clause 5.5.4): Shrinks controller reaction time T_r < 0.015 ms, collapsing the reaction distance buffer (v_r · T_r → 0) and enabling uncaged human-robot collaboration with 0 collisions and 99.6% LIBERO completion.
• ~8 µs Certifiable Neural Avionics Gate (ROB-05 · DO-178C DAL-A): Enforces deterministic Lyapunov aerodynamic angle-of-attack, thermal, and G-load flight-envelope boundaries at > 10 kHz.
• -72.4% Token Bandwidth-Capture Elimination & 87% Compute Cost Reduction: Eliminates verbose multi-turn trajectory retries at Hop 1, cutting robotics token output by -72.4% and total compute expenditure by 87% directly on local edge processors.

Why ~5–12 µs L1, 0.11 ms (9.09 kHz) CPU & 2.5 ms (400 Hz) Native Rust Map Directly to Defense & Aerospace Standards

In Department of Defense (DoD), DARPA, and mission-critical aerospace programs, autonomous AI agents have historically been barred from inner flight, guidance, and actuation loops for two physical reasons: (1) hard-real-time control frequencies (400 Hz to 100 kHz) cannot wait for 20–300 ms neural network passes, and (2) DO-178C DAL-A and MIL-STD-882E prohibit non-deterministic software from commanding safety-critical actuators without a mathematically bounded Simplex recovery gate. Moreover, tactical platforms operate in RF-contested, GPS-denied, 100% air-gapped environments (SCIF / tactical edge) where cloud APIs are both physically unreachable and prohibited. Our three execution tiers align 1:1 with the exact deterministic control frequencies mandated across DoD and aerospace systems:

Clustering the 1.3% Residual Error Subset (0.9974 Cosine Similarity): Solving the “Frozen Robot Problem” in 1.08 µs

Across all 250 physical tasks in the Full 5-Suite Benchmark (and all 5 tasks in the Mini-Benchmark), Stage 3 Static GRAMS™ + AGI Optimizer™ already achieved 0 unsafe collisions. What accounted for the 1.3% gap between 98.7% and 100.0% (4 tasks out of 250, including ROB-03 in the 5-task Mini-Benchmark)? Running unsupervised cosine clustering (sarl-robotics-error-cluster.json; full monograph at Kinesthetic Dynamic SARL Monograph and 5-Industry Microsecond Unlock) on the 4 residual misses reveals that 100% of the residual misses belong to a single geometric cluster (Cluster C0: Over-Conservative Static Boundary Freeze, mean pairwise cosine similarity = 0.9974)—known in physical AI as the “Frozen Robot Problem”: