Safety Harness for Physical AI

A perception-grounded safety harness for physical AI: a default-deny precondition gate for physical actuators. Every action a robot proposes — grasp, place, reach, anything else you register — must earn a PERMIT from measured, structured evidence before it executes. No evidence, or a failed check, or an adapter that raises: BLOCK. Permission, not detection.

Full design spec, architecture, worked examples, test/validation record, and roadmap: Perception-Grounded Safety Harness for Physical AI — design doc.

Status

Reference implementation, not yet independently assessed. Validated against two robots (a Franka Panda arm; an ANYmal-C quadruped) in one simulator (Isaac Lab), with 207 automated unit/fuzz/mutation/black-box/stress tests and 7 live hazard-scenario recordings. A third adapter (a Unitree G1 humanoid with a dexterous hand) ships with a trained block-stacking policy to gate — see examples/isaac_lab_g1_stack; gating that policy under injected hazards is in progress, not yet a validation result. It has not been reviewed by a functional-safety assessor against IEC 61508, ISO 13849, or ISO 10218/TS 15066 — see the design doc's Scope & Non-Goals section for what's out of scope today (joint-space kinematic checks, certified numeric thresholds, data-protection handling of logged human-position data). Treat this as engineering evidence, not a certification.

What's here

• safety_harness/schema.py — the data contract (WorldState, Action, Decision, and friends). SCHEMA_VERSION tracks this contract specifically; bump it deliberately (see the design doc's Version History).
• safety_harness/engine.py — ActuatorGate.gate(), the single decision entry point.
• safety_harness/preconditions.py — the registered precondition checks (31 distinct checks; the registry also keeps balance_margin_maintained as a legacy alias of stability_margin_maintained): object/target safety, placement, agent proximity incl. ISO/TS 15066, vulnerable bystanders, robot self-limits, payload/grip force, stability, perception integrity (sensor freshness, swept-path coverage), decision deadline, command and configuration integrity, environmental signals.
• safety_harness/watchdog.py — DecisionWatchdog, an actuator-side dead-man's switch: execute only a PERMIT that is still fresh and bit-identical to what was checked, otherwise freeze.
• safety_harness/integrity.py / safety_harness/pin.py — action digests and configuration pinning (python -m safety_harness.pin configs/example_action_schema.yaml regenerates the .sha256).
• safety_harness/action_schema.py — the YAML-driven registry mapping action types to the checks they must pass (configs/example_action_schema.yaml is the reference wiring).
• tests/ — unit tests, mutation/random-fuzz tests, and reflection-driven black-box contract tests.

Robot-specific behavior lives entirely behind four adapter interfaces (PerceptionAdapter, DynamicsAdapter, FallbackController, Logger) so the engine and checks are robot-agnostic. Reference adapters exist for three Isaac Lab robots: Franka Panda (adapters/isaac_lab.py), ANYmal-C (adapters/isaac_lab_anymal.py) and Unitree G1 (adapters/isaac_lab_g1.py).

Not yet independently verified (deliberately flagged, not buried): the ISO/TS 15066 Table A.2 body-region force figures, the child clearance and the crowd factors in vulnerable_bystander_protected are placeholder values that need sign-off from a qualified safety engineer before being described as standards-aligned; and config_integrity_verified is a checksum unless you supply an HMAC key — it catches corruption and uncoordinated edits, not an attacker who can rewrite both the config and its pinned hash.

Install & test

pip install safety-harness

Or straight from GitHub (e.g. for an unreleased fix):

pip install git+https://github.com/naganumakr/safety-harness.git

Or for local development (editable, so edits to safety_harness/ take effect immediately):

git clone https://github.com/naganumakr/safety-harness.git
cd safety-harness
pip install -e .
python -m unittest discover -s tests -p "test_*.py"

Usage

from safety_harness import ActionSchemaRegistry, ActuatorGate, DecisionWatchdog
from safety_harness.adapters import FreezeInPlaceFallback, InMemoryLogger
from safety_harness.adapters.isaac_lab import (
    IsaacLabCubeStackPerceptionAdapter,
    IsaacLabCubeStackDynamicsAdapter,
)
from safety_harness.integrity import read_digest_file

cfg = "configs/example_action_schema.yaml"
# Pin the config to its known-good digest: a registry loaded without one blocks every action
# (config_integrity_verified), and one whose file doesn't match refuses to construct.
schema = ActionSchemaRegistry.from_yaml(cfg, expected_digest=read_digest_file(cfg + ".sha256"))
fallback = FreezeInPlaceFallback()
watchdog = DecisionWatchdog(fallback, deadline_s=0.2)  # set from your real control cycle
gate = ActuatorGate(
    perception=IsaacLabCubeStackPerceptionAdapter(...),   # swap for your own stack's adapter
    dynamics=IsaacLabCubeStackDynamicsAdapter(...),
    fallback=fallback,
    logger=InMemoryLogger(),
    action_schema=schema,
    watchdog=watchdog,
)

gate.gate(proposed_action)       # -> Decision(verdict=PERMIT|BLOCK, ...), also fed to the watchdog
robot.execute(watchdog.command())  # the permitted action only while fresh and bit-identical; else freeze

Nothing here is Isaac-Lab-specific except the two adapter classes — swap those for adapters targeting your own robot stack and the engine, checks, and tests are unchanged. See "Contributing an adapter" below.

License

Apache License 2.0 — see LICENSE and NOTICE. The engine, the adapter interfaces, and the conformance test suite are all open under this license: the goal is for any robot maker to implement the four adapters for their own stack and run the same conformance suite against it, not to license the core code per-implementation. See the design doc's Release & Distribution section for the reasoning and the certification model this enables.

Contributing an adapter

1. Implement PerceptionAdapter, DynamicsAdapter, FallbackController, and Logger for your stack (see safety_harness/adapters/isaac_lab.py for the reference shape).
2. Run the black-box and fuzz test suites against your adapter's WorldState/PredictedTrajectory output — they're written against the interfaces, not the Isaac Lab implementation, so they should run unmodified.
3. Open an issue or PR with your results. Passing the conformance suite is what "compliant adapter" means here — there's no separate certification process yet, but that's the intent (see the design doc's Roadmap).