rskill-robometer-4b-nf4

OpenRAL rSkill — Robometer-4B (Qwen3-VL-4B robotic reward foundation model) packaged as an NF4 bitsandbytes reward rSkill (ADR-0057). Given a rollout's RGB frames plus the task instruction, it emits per-frame normalized progress (0–1) and per-frame success probability, queried on demand by the Reasoner. No actuators. Advisory-only. Apache-2.0.

Quick Start

ral skill install hf://OpenRAL/rskill-robometer-4b-nf4

from openral_core.schemas import RSkillManifest

manifest = RSkillManifest.from_yaml("rskills/robometer-4b/rskill.yaml")
assert manifest.kind == "reward"
assert manifest.role == "s2"
assert manifest.reward.progress_range == (0.0, 1.0)
assert manifest.quantization.extra["scheme"] == "nf4"
assert manifest.is_commercial_use_allowed is True

What It Does

Robometer is a general-purpose robotic reward model trained on RBM-1M (>1M trajectories across diverse embodiments, including failures) with a dual objective: a frame-level progress loss anchored on expert data and a trajectory-comparison preference loss for global ordering. Given a task instruction and a rollout video, it predicts per-frame progress (continuous values over time) and per-frame success probability.

This rSkill declares kind: reward and role: s2: it is a pure perception consumer operating at S2 (slow-reasoning) rate (~0.2–1 Hz), not an S1 fast policy. It runs in parallel with a kind: vla policy, continuously ingesting the VLA's camera frames into a rolling window, and the Reasoner queries it on demand — "how is success doing now / over the last X seconds?" — to decide whether to continue, escalate to a scene VLM (query_scene), advance to the next subgoal, or enter the replanning ladder. It never drives ros2_control joints and never gates motors (CLAUDE.md §1.1).

Why a reward model alongside the VLA

A VLA policy emits actions but has no notion of whether it is succeeding. Robometer closes that loop: it turns the camera stream into a normalized per-frame progress + success signal the Reasoner can act on, so a stalled or failing rollout triggers replanning instead of running to a timeout.

Architecture

Robometer-4B finetunes Qwen/Qwen3-VL-4B-Instruct (model_type: qwen3_vl) with three prediction heads — progress_head, success_head, preference_head — on top of a frame-pooled attention readout (frame_pool_attn). The on-disk HF config.json advertises architectures: ["RFM"], but the actual model class is RBM (in the upstream robometer package). It has no auto_map and ships no Hub-side modeling code, so vanilla transformers.AutoModel cannot load it — the sidecar loads it via the pinned robometer package (robometer.utils.save.load_model_from_hf).

Runtime

The kind: reward runtime is implemented as a read-only Reasoner tool (QueryTaskProgressTool), not an ExecuteSkill (a reward monitor produces scalars, not actions):

• Sidecar: an out-of-process ZMQ REQ/REP + msgpack server boots the NF4 model in its own isolated venv, maintains a rolling time-indexed frame buffer (frame_window_s), and answers windowed progress/success queries. It loads via robometer.utils.save.load_model_from_hf with transformers pinned to 4.57.1 (5.x changes the processor __call__ kwargs and drops input_ids) and the robometer package pinned to commit a669dffc.
• Frame source: abstracted for sim and real. The sidecar consumes the same sensor_msgs/Image camera topic the co-active VLA uses — fed by the GStreamer perception tee on real hardware, or by the sim HAL camera publisher in deploy-sim (which has no GStreamer). In deploy-sim only camera-rendering robots expose frames; absent frames surface as ROSPerceptionStale.
• Reasoner tool: the LLM sees the read-only query_task_progress tool when a reward rSkill is co-active with a VLA. It asks for the windowed assessment (progress_now, success_now, trends, stalled) and the answer feeds the next reasoning tick / the replanning ladder.

Inference contract

Discrete (binned) mode yields the normalized signal OpenRAL consumes: compute_batch_outputs(..., sample_type="progress", is_discrete_mode=True, num_bins=100) returns progress_pred (per-frame ∈ [0,1]) and outputs_success["success_probs"] (per-frame ∈ [0,1]). Continuous mode returns raw, unnormalized regression values instead. Default sampling is 3 fps.

Validated live

End-to-end on an NVIDIA RTX 4070 Laptop (8 GB) (ADR-0057 Phases 0/2/3):

• NF4 quantization: 236 Linear modules → Linear4bit; 8.91 GB bf16 → 3.33 GB resident, 3.56 GB peak including an 8-frame forward — 4.44 GB headroom for a co-resident small NF4 VLA.
• Working sidecar: streaming a real rollout video ("Put green stick in brown bowl") through the ZMQ sidecar, progress ramped 0.21 → 0.88 and success spiked to 0.90 exactly at task completion, then eased — exactly the Reasoner signal intended.

Run with PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True. The model loads via the robometer package (not AutoModel); the sidecar venv pins transformers==4.57.1.

Benchmark Numbers

Paper-reported (Robometer team, March 2026, arXiv 2603.02115); reproduced_locally: false. Robometer reports more generalizable reward functions than prior methods (GVL, VLAC, RoboDopamine, TOPReward) across benchmarks and real-world evaluations, improving downstream robot-learning performance. See the paper for the full tables.

Supported robots and embodiments

This reward monitor is embodiment-agnostic — it scores camera frames + a task instruction and emits scalars, never actuator commands, so it imposes no kinematic requirement. The only hardware dependency is an RGB camera stream of at least 224×224. It pairs with any S1 VLA policy: the VLA acts, this model reports whether the task is progressing / has succeeded.

Sensors and Observation Contract

Direction	Key	Modality	Shape / format	Notes
in	any RGB camera	RGB video frames	min 224 × 224	the same topic the co-active VLA consumes
in	task instruction	text	natural language	required (instruction_required: true)
out	progress	float per frame	∈ progress_range ([0,1])	normalized task progress
out	success	float per frame	∈ [0,1]	per-frame success probability

The model emits no action chunks and has no proprioception contract.

Manifest Summary

Field	Value
name	OpenRAL/rskill-robometer-4b-nf4
version	0.1.0
license	apache-2.0
role / kind	s2 / reward
runtime	pytorch
quantization.dtype / scheme	int4 / nf4
weights_uri	hf://OpenRAL/rskill-robometer-4b-nf4 (pre-quantized NF4, meta-loadable; built from the SHA-pinned upstream source_repo)
min_vram_gb.bf16	9.0 GB
min_vram_gb.int4	3.6 GB
reward.frame_window_s / target_fps	40.0 s / 3.0 fps (ADR-0074 amendment — scores the whole attempt start→now, not an 8 s trailing slice)
reward.progress_range / success_threshold	[0,1] / 0.5
latency_budget.per_chunk_ms	3000 ms
actions	monitor

License

The rSkill package metadata and README are OpenRAL project files under Apache-2.0. The wrapped Robometer-4B weights are released under Apache-2.0, permitting commercial use. No OPENRAL_ALLOW_NONCOMMERCIAL=1 flag is needed. The upstream robometer code (loaded by the sidecar) is governed by its own repository license; it is executed in an isolated, pinned sidecar venv and is not an OpenRAL-trusted org (see _vendor/PROVENANCE.md).