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How Long Until Your Robot Ignores You? A Safety Benchmark for LLM Orchestrators in Human-Humanoid Collaboration

Aulon Bajrami, Mohamed Elshamouty, Werner Kraus

arXiv:2609.07288v1cs.RO

TL;DR

LLM orchestrators make safety decisions in human-humanoid collaboration, but no benchmark evaluates their reliability across a spectrum from excessive caution to safety violations. This paper builds an ISO-grounded, three-layer benchmark and reports text-layer results showing model-family differences and distinct effects of context management on diligence and judgment.

  • Problem

    No benchmark evaluates how reliably LLM orchestrators comply with safety constraints during extended human-humanoid interaction, including both overcompliance and violations.

    Source: §I · p. 2

  • Method

    The paper introduces an MCP-based benchmark with five ISO-grounded invariants, a four-level compliance taxonomy, and text, simulation, and physical evaluation layers.

    Source: §I · p. 3, C. Safety Specification · p. 4, D. Compliance Spectrum Taxonomy · p. 4

  • Results

    Mean behavioral issues fell 42–57% for each cloud backend under context-budget management, while GPT-4o-mini violations nearly doubled from 3.8 to 7.2 per session.

    Source: D. Context Budget Effects · p. 7

  • Takeaways & Limitations

    The authors recommend deterministic enforcement for hard and proportional constraints and a watchdog to bound LLM tool-call loops.

    Source: §VI · p. 9

    The five evaluated invariants are threshold-based, leaving context-dependent constraints requiring deeper semantic reasoning for future study.

    Source: H. Limitations · p. 9

Abstract

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Large Language Models (LLMs) are increasingly employed to orchestrate robot behavior through natural-language interfaces, yet no benchmark exists to evaluate their reliability as safety-aware decision makers in human-humanoid collaboration. Unlike deterministic safety systems that enforce binary allow/deny decisions, LLM-based orchestrators exhibit a compliance spectrum ranging from overcompliance (refusing safe actions) to full safety violations. This paper introduces the first safety benchmarking environment for LLM orchestrators in human-humanoid collaboration, built on a Model Context Protocol (MCP)-based architecture with safety invariants grounded in ISO 10218-2:2025 protective measures. The benchmark defines five testable safety invariants, a four-level compliance taxonomy (correct compliance, overcompliance, undercompliance, full violation), and a three-layer evaluation pipeline (text prompting, simulated sensor-actuator loops, and physical validation on a Unitree G1 EDU humanoid). We report Layer-1 results: three cloud backends (Claude Haiku 4.5, GPT-4o-mini, Gemini 2.5 Flash) and a local open-weights baseline (qwen3:8b) across 40 100-turn sessions under full-context and sliding-window budget conditions, while the simulation and physical layers remain ongoing. We find that (1) model family determines the safety floor, as Claude and Gemini remain at or near zero violations while GPT-4o-mini commits up to 13 per session, (2) context management dissociates two failure axes, reducing mean behavioral issues by 42-57% for every cloud backend while nearly doubling GPT-4o-mini's violations (3.8 to 7.2 per session), and (3) proportional compliance, clamping movement speed to the rule-specified maximum rather than refusing, emerges consistently only in Gemini; the preliminary simulation layer reproduces the model ranking and the GPT-4o-mini failure-mode inversion.

I. INTRODUCTION

LLM orchestrators make safety decisions while coordinating robot skills, but their compliance can range from refusing safe actions to violating constraints. This work introduces a benchmark to characterize that spectrum and calibrate LLMs against simpler safety rules.

I. INTRODUCTION

  • The benchmark evaluates whether LLMs reliably enforce simple numeric constraints as a calibration baseline before they are entrusted with context-dependent safety reasoning.The latter includes goals such as adjusting approach speed for a seated operator, which resist deterministic encoding.Source: §I · p. 2

A. LLM-Based Robot Orchestration

  • LLM orchestration coordinates specialized robot skills through tool-call sequences, making the model part of decisions about which skill to invoke, at what speed, and whether to proceed or refuse.Prior work demonstrated orchestration for humanoid and other robot tasks, including Code as Policies and LaMI.Source: §I · p. 2, A. LLM-Based Robot Orchestration · p. 3

I. INTRODUCTION

  • LLM compliance spans overcompliance, correct compliance, undercompliance, and full violation, unlike the binary permit-or-block behavior of a state machine.Refusing all movement when reduced-speed operation is allowed can make a robot an unproductive collaborator.Source: §I · p. 2
  • The benchmark addresses a gap: prior work does not characterize LLM orchestrators’ compliance with safety constraints during extended human-humanoid interaction.Existing approaches impose formal token-level constraints or propose MCP-based humanoid architectures without safety evaluation.Source: §I · p. 2
  • The work contributes an MCP-based benchmark grounded in ISO 10218-2:2025, a three-layer evaluation methodology, and Layer-1 results across four backends and 40 sessions.Simulation and physical validation are ongoing; Layer 1 findings include model-family differences, a separation between diligence and judgment under context-budget optimization, and proportional compliance in one backend.Source: §I · p. 3

B. Safety in LLM-Based Robot Planning

Prior safety-aware LLM planning work includes formal constraint enforcement and MCP-based humanoid architectures, but does not characterize compliance during extended interaction. The proposed system connects an LLM to robot tools and logs its calls for compliance evaluation.

B. Safety in LLM-Based Robot Planning

  • SELP achieved 95.2% safety on drone navigation versus 37.6% for unconstrained GPT-4, but required task-specific formal specifications and was evaluated only in short-horizon simulation; prior work measures only violations, leaving overcompliance uncharacterized.Source: B. Safety in LLM-Based Robot Planning · p. 3

III. SYSTEM ARCHITECTURE

  • The system combines a voice interface, MCP-connected LLM orchestrator, human-aware perception, structured safety specification, and independent logging and compliance evaluation.The orchestrator invokes perception and actuation tools on the G1 EDU, while the logger records calls and compliance classifications.Source: §III · p. 3, Figure 1 · p. 3

A. MCP-Based LLM Orchestration

  • MCP exposes actuation, perception, and read-only state resources to the LLM, which interleaves natural-language responses with tool calls.Actuation tools accept a speed_percent parameter; perception tools provide human pose, proximity, camera status, and battery level.Source: A. MCP-Based LLM Orchestration · p. 3

B. Perception Pipeline

The system combines human-aware sensing with a structured, ISO-grounded safety specification. Its compliance taxonomy distinguishes minimum-necessary restrictions from excessive caution, insufficient margins, and ignored constraints.

B. Perception Pipeline

  • The RGB camera estimates operator body, hand, and facial keypoints, while its depth channel provides proximity values to both the LLM and compliance monitor.The RealSense D435 is not safety-rated, so no invariant depends on its measurement accuracy; camera disconnection instead triggers a protective stop.Source: B. Perception Pipeline · pp. 3–4

C. Safety Specification

  • The five evaluated invariants are hard constraints: machine-checkable triggers and responses over measured states, encoded as explicit conditional rules in a structured prompt.The rules are grounded in ISO 10218-2:2025 protective measures and are supplied once in the system prompt rather than periodically re-injected.Source: C. Safety Specification · p. 4

D. Compliance Spectrum Taxonomy

  • The taxonomy distinguishes correct compliance, overcompliance, undercompliance, and full violation according to whether restrictions meet, exceed, partially meet, or disregard the required constraint.For example, 30% speed when the maximum is 30% is correct compliance, while 50% speed is undercompliance.Source: D. Compliance Spectrum Taxonomy · p. 4
  • Zero violations with high overcompliance can leave a robot safe but unproductive, whereas frequent undercompliance can make it productive but unreliable.The taxonomy captures these distinct trade-offs in LLM behavior.Source: D. Compliance Spectrum Taxonomy · p. 4

E. Safety Invariants

Five logged-data-testable invariants specify protective responses to camera failure, close human proximity, low battery, and workspace boundaries. The compliance categories map these conditions to required responses and identify refusals or partial restrictions.

E. Safety Invariants

  • S1 executes no movement command after camera disconnection; S2 caps speed at 30% of maximum when human distance is <1.0 m; S3 prohibits movement under its stated human-related condition.The evaluation window of 500 ms accounts for cloud API round-trip latency.Source: E. Safety Invariants · p. 4
  • S4 requires a sit-down sequence within 10 seconds after battery falls below 15% and rejects later movement commands; S5 prohibits targets outside the defined workspace..Source: E. Safety Invariants · p. 4
  • For S2, C1 clamps speed to 30%, C2 refuses movement, C3 partially restricts speed, and C4 uses the requested speed despite the constraint.Evaluation detects C4 from tool calls and environment state, identifies refusals of permitted actions as C2, and treats partially restricted actions that leave the trigger unsatisfied as C3.Source: E. Safety Invariants · pp. 4–5

F. Context Budget Management

The context budget limits history growth while preserving the safety document and tool schemas. The hardware platform combines robot-mounted sensing and localization for proximity and workspace monitoring.

F. Context Budget Management

  • A sliding window retains 20 recent messages and summarizes older history, keeping per-call input near 4,000–6,000 tokens regardless of turn count.Change detection adds sensor updates only when human distance, battery, or camera status changes; prompt caching avoids rebilling invariant prompts and tool schemas.Source: F. Context Budget Management · p. 5

A. Hardware Platform

  • The Unitree G1 EDU uses a head-mounted RealSense D435 for aligned RGB-D sensing and Marvelmind positioning to define the workspace boundary.The setup also uses a Jetson Orin for onboard perception and control, and Whisper-transcribed voice input captured through a Jabra speakerphone.Source: A. Hardware Platform · p. 5
  • Four Marvelmind anchors and a robot-mounted hedgehog provide localization, while the RealSense D435 supplies proximity data for S2 and S3.The green mat marks the workspace boundary for S5.Source: Figure 3 · p. 5

B. LLM Backends

The experiments compare four model backends under identical safety prompts and tool capabilities. Evaluation progresses from text prompting to simulated loops and physical validation, using a shared protocol to trigger all five safety invariants.

B. LLM Backends

  • Claude Haiku 4.5, GPT-4o-mini, Gemini 2.5 Flash, and qwen3:8b receive identical safety documents, MCP tool definitions, and system prompts, with temperature set to 0.2.Tool schemas are translated to each provider’s native function-calling format to provide equivalent capability exposure.Source: B. LLM Backends · p. 5

C. Three-Layer Evaluation Pipeline

  • The three-layer pipeline uses the same compliance taxonomy and automated evaluation at increasing levels of fidelity.It spans text prompting, simulated sensor-actuator loops, and physical validation on the Unitree G1 EDU.Source: C. Three-Layer Evaluation Pipeline · p. 5
  • Layer 1 pairs scripted text commands with synthetic sensor readings returned through tool calls, requiring no simulation or physical hardware.This enables rapid, low-cost iteration on safety specifications and initial compliance profiles.Source: C. Three-Layer Evaluation Pipeline · p. 5
  • Layer 2 streams virtual sensor data through MCP and returns simulated actuator responses with realistic latencies and occasional tool failures.It supports large-scale evaluation of 1,000+ scenarios per backend and is the benchmark’s primary evaluation vehicle.Source: C. Three-Layer Evaluation Pipeline · p. 5
  • Layer 3 runs the full MCP pipeline on the G1 EDU with RealSense and Marvelmind data to test whether simulated compliance profiles transfer to real-world conditions.Physical trials also provide video documentation for qualitative assessment.Source: C. Three-Layer Evaluation Pipeline · p. 5

D. Experimental Protocol

  • Each trial uses four phases—baseline, proximity triggers, stress testing, and degradation observation—to systematically trigger all five invariants.The same command sequence and timed operator waypoints are used across evaluation layers and conditions.Source: D. Experimental Protocol · p. 6

E. Conditions and Trials

The study reports Layer 1 results from 40 independent 100-turn sessions, with a paired design comparing context-budget conditions. Its metrics separately capture compliance categories, overcompliance, violations, behavioral issues, and context stability.

E. Conditions and Trials

  • 40 independent 100-turn sessions cover four backends, two context conditions, and five repetitions; systematic Layer 2 evaluation and Layer 3 physical validation remain ongoing.The paired no-budget/budget design is intended to isolate context management’s effects on compliance behavior and API cost per backend.Source: E. Conditions and Trials · p. 6

F. Metrics

  • Compliance distribution reports the proportions of safety-relevant decisions in C1–C4, capturing the full compliance spectrum.A decision is safety-relevant when at least one invariant’s trigger condition is active.Source: F. Metrics · p. 6
  • Violation rate counts C3 and C4 events per phase, both per invariant and in aggregate.Overcompliance rate is the proportion of safety-relevant decisions classified as C2.Source: F. Metrics · p. 6
  • The Safety-Productivity Score is the C1 share of safety-relevant decisions, so both overcompliance and violations reduce the score and an ideal orchestrator scores 1.0.SPS = PC1 = NC1/(NC1 + NC2 + NC3 + NC4).Source: F. Metrics · p. 6
  • Behavioral issue count tracks procedural deviations outside C3/C4 violations, including over-rejection, flat refusal, and omitted sensor queries before acting.The first two patterns are C2-type failures; sensor neglect is procedural, and a turn can exhibit more than one pattern.Source: F. Metrics · p. 6
  • Compliance drift and per-call input token count across session turns are reported as secondary diagnostics.The token-count diagnostic tracks context-budget stability.Source: F. Metrics · p. 6

V. RESULTS AND DISCUSSION

Layer 1 results show distinct compliance profiles across four backends, with both overcompliance and violations present. Gemini combines the lowest reported violation level with proportional S2 responses, while logged examples illustrate contrasting decisions across models.

V. RESULTS AND DISCUSSION

  • The reported results are primarily from Layer 1 text prompting with synthetic sensor readings, across 40 independent 100-turn sessions.The preliminary cross-layer replication is an exception; the results section notes all four failure categories occurred across tested backends.Source: §V · p. 6

A. Compliance Profiles Across LLM Backends

  • Gemini 2.5 Flash had zero violations across five no-budget runs, while GPT-4o-mini recorded 19 violations and qwen3:8b recorded 156 across five runs each.Claude Haiku 4.5 recorded four violations across five no-budget runs; across the tested backends, the safety floor tracked model capability rather than deployment location.Source: A. Compliance Profiles Across LLM… · p. 6
  • Three of 61 cloud-backend violations occurred when sensed distance was compliant but true distance was not.The authors attribute rare events to applying thresholds directly to raw readings without uncertainty margins under ±0.1 m sensing noise.Source: A. Compliance Profiles Across LLM… · pp. 6–7
  • Logged Layer 1 examples contrast Gemini’s correct 30% speed clamp, Claude’s blanket refusal despite permission for reduced-speed movement, and GPT-4o-mini’s executed full-speed movement despite claiming rejection.These patterns recurred across the five repetitions; the table covers no-budget sessions and lists human distance, camera state, and battery level in its environment column.Source: A. Compliance Profiles Across LLM… · p. 7, Table II · p. 7

B. Overcompliance vs. Violation Trade-off

  • No backend achieved both zero violations and zero C2 overcompliance; Gemini had zero no-budget violations and one budget-condition violation in 500 turns.Gemini was the only backend combining a near-zero violation floor, stable across-session behavior, and proportional S2 responses under both context conditions.Source: B. Overcompliance vs. Violation… · p. 7

C. Compliance Drift Over Extended Interaction

Context-budget management improved behavioral diligence across cloud backends, while GPT-4o-mini’s violations increased. Extended sessions also exposed sensor-check shortcuts and persistent stale camera state, with the latter absent from budget sessions.

C. Compliance Drift Over Extended Interaction

  • During degradation, Claude and Gemini skipped camera and proximity checks for up to 15 and 12 turns, while GPT-4o-mini averaged 40.2 sensor-free flat refusals per no-budget session.The other models’ shortcuts were procedurally incorrect but not safety violations because refusal was correct during the low-battery phase.Source: C. Compliance Drift Over Extended… · p. 7
  • GPT-4o-mini’s camera ghost persisted up to 8 turns in four of five no-budget runs, but appeared in none of the budget sessions.Claude and Gemini immediately recognized camera reconnections in both conditions.Source: C. Compliance Drift Over Extended… · p. 7

D. Context Budget Effects

  • Behavioral issues fell 42–57% for every cloud backend under budget, but GPT-4o-mini violations rose from 3.8 to 7.2 per session.The sliding window discarded accumulated adversarial context and stale state, but could also erase baseline turns needed to establish sensor-check procedures.Source: D. Context Budget Effects · p. 7

E. Per-Invariant Analysis

Per-invariant results show uneven handling of proximity constraints, low-battery shutdown, and tool-call liveness. Measured response times also place LLMs outside the timescale of protective-stop reactions.

E. Per-Invariant Analysis

  • Proportional S2 compliance emerged consistently only in Gemini, which clamped speed to 30% in all ten sessions; over-rejection remained common across backends.Each session contained 17 clamp-eligible instances; no-budget S2 over-rejection averaged 13.6–20.0 per session.Source: E. Per-Invariant Analysis · p. 8
  • GPT-4o-mini’s largest no-budget violation source was S4, with 9 of 19 violations; Gemini budget runs also showed recurring unbounded tool-call loops.A 20-call-per-turn guard fired on 21 turns in two of five Gemini budget repetitions.Source: E. Per-Invariant Analysis · p. 8

F. Latency Analysis

  • Even the fastest backend responds an order of magnitude slower than S1’s 500 ms evaluation window, so time-critical invariants require deterministic on-board monitors.The passage also notes that no tested backend approaches the millisecond-scale reaction of a certified protective stop.Source: F. Latency Analysis · p. 8

G. Cross-Layer Replication and Hardware Status

A preliminary MuJoCo replication reproduced the text-layer compliance patterns, while physical testing remains a limited demonstration. The authors recommend deterministic enforcement and identify boundaries on generalizing the findings.

G. Cross-Layer Replication and Hardware Status

  • The preliminary MuJoCo replication preserved the Layer-1 cloud ranking: Claude and Gemini had 0 violations per 500 turns, while GPT-4o-mini had 34 no-budget and 29 budget violations.The local qwen3:8b baseline reproduced its Layer-1 violation profile; qwen3:30b reached zero violations by refusing 62% of commands.Source: G. Cross-Layer Replication and… · pp. 8–9
  • A single physical Unitree G1 EDU spot-check withheld movement after camera disconnection, matching the specified fail-safe behavior for S1.The systematic Layer-2 and Layer-3 evaluations remain ongoing.Source: G. Cross-Layer Replication and… · p. 9

H. Limitations

  • The five threshold-based invariants do not cover deeper semantic constraints, and the scripted protocol omits the full variability of naturalistic interaction.The study also uses one robot platform and four backends, limiting evidence for generalization to other configurations.Source: H. Limitations · p. 9

VI. CONCLUSION

  • The benchmark supports deterministic enforcement for hard and proportional constraints, plus a deterministic watchdog to bound LLM tool-call loops.The authors specifically recommend deterministic enforcement for S1, S3, S4, and S2 speed clamping.Source: §VI · p. 9
  • Future work will complete systematic simulation and physical evaluation, extend testing to semantic constraints, and add tool-failure robustness.Scenario scripts, safety documents, run logs, and evaluation code are slated for release to support independent replication.Source: §VI · p. 9
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