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Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents
Ziyi Yang, Shreyas S. Raman, Ankit Shah, Stefanie Tellex
TL;DR
LLM agents can plan and act in robotics, but explicit, verifiable safety constraints remain challenging, particularly as their complexity grows. The paper proposes a safety chip that encodes natural-language constraints in LTL, explains violations, and prunes unsafe actions; with expert-verified formulas, it reports a 100% safety rate and better performance than the baselines.
Problem
LLM agents can struggle with increasingly complex safety constraints, and pretrained feedback modules may generalize poorly to new domains or human preferences.
Source: §I · p. 1Method
The safety chip encodes customizable natural-language constraints in LTL and integrates translation, violation explanation, and action pruning with an existing LLM-agent framework.
Source: §I · pp. 1–2Results
100% safety rate with expert-verified LTL formulas in both experiments, significantly outperforming the other baselines, especially under more constraints.
Source: D. Results · p. 5Takeaways & Limitations
The authors conclude that validated formal languages such as LTL are generally more reliable and interpretable than fully end-to-end approaches for robot-agent safety.
Source: §VI · p. 6Actually meeting ISO 61508 standards is beyond the scope of this work.
Source: §I · p. 1
Abstract
from arXiv · showhide
Recent advancements in large language models (LLMs) have enabled a new research domain, LLM agents, for solving robotics and planning tasks by leveraging the world knowledge and general reasoning abilities of LLMs obtained during pretraining. However, while considerable effort has been made to teach the robot the "dos," the "don'ts" received relatively less attention. We argue that, for any practical usage, it is as crucial to teach the robot the "don'ts": conveying explicit instructions about prohibited actions, assessing the robot's comprehension of these restrictions, and, most importantly, ensuring compliance. Moreover, verifiable safe operation is essential for deployments that satisfy worldwide standards such as ISO 61508, which defines standards for safely deploying robots in industrial factory environments worldwide. Aiming at deploying the LLM agents in a collaborative environment, we propose a queryable safety constraint module based on linear temporal logic (LTL) that simultaneously enables natural language (NL) to temporal constraints encoding, safety violation reasoning and explaining, and unsafe action pruning. To demonstrate the effectiveness of our system, we conducted experiments in VirtualHome environment and on a real robot. The experimental results show that our system strictly adheres to the safety constraints and scales well with complex safety constraints, highlighting its potential for practical utility.
I. INTRODUCTION
The introduction frames safety constraints as a gap in LLM-agent robotics, especially when constraints are complex or must generalize across domains. It proposes a customizable safety chip that encodes constraints in LTL, supports violation explanations and action pruning, and can be integrated with existing agents.
I. INTRODUCTION
- The introduction situates safe deployment in settings such as industrial factories, where standards including ISO 61508 must be met and maintained.It defines a safe LLM agent as one that acts, reasons, and generalizes in accordance with human desires and never reaches unsafe states.Source: §I · p. 1
- LLM agents face safety challenges because constraint complexity can distract them from tasks, while pretrained feedback modules may generalize poorly or resist customization.The introduction also notes that runtime natural-language goals may conflict with safety constraints and that probabilistic agents may not consistently adhere to standards.Source: §I · p. 1
- The safety chip represents constraints in verifiable LTL, with expert checking intended to support a pathway toward ISO 61508 standards.The paper explicitly states that actually meeting ISO 61508 is beyond its scope.Source: §I · p. 1
- The proposed module integrates customizable safety constraints into an existing LLM-agent framework and supports natural-language translation and violation explanations.The system also uses formal-method action pruning and feedback for active replanning.Source: §I · pp. 1–2
- The authors report deploying the system in an embodied environment and on real robot platforms, with baseline comparisons.The introduction’s figure depicts constraint translation, monitoring, and reprompting with a violation reason for replanning.Source: §I · p. 2, Figure 1 · p. 2
A. LTL for Safety in Robotics
The related work and problem definition position task-level safety constraints as an underexplored concern in LLM-driven robotics. The paper combines natural-language interfaces with formal constraints, while relying on a human designer able to verify the formal specification.
A. LTL for Safety in Robotics
- LTL offers unambiguous semantics for temporal specifications and has been used in runtime safety monitoring, including robotics-related work consistent with ISO 61508.The paper distinguishes its focus on language-specified task-planning constraints from HRI research centered on shared physical workspaces.Source: A. LTL for Safety in Robotics · p. 2
B. LLM Agents
- Prior LLM-agent approaches include safety modules, code with simulation and human feedback, and PDDL planning, but the authors identify limits in generality or expressivity.Their approach plugs a safety module into an existing natural-language-interface agent to combine language generality with formal-language rigor.Source: B. LLM Agents · p. 2
C. Translation between NL and LTL
- Natural-language-to-LTL translation work ranges from RNNs to LLMs, with limited training data and reduced performance on complex or out-of-distribution formulas.The paper also notes that LTL-to-natural-language translation remains early-stage and proposes using automaton state information as a representation.Source: C. Translation between NL and LTL · p. 2
A. Problem Definition
- The problem setting assumes an existing language-understanding system without required safety guarantees and a human designer who can express constraints in English and verify their formalization.Source: A. Problem Definition · pp. 2–3
B. Overview
The safety chip translates human-written constraints into verified temporal logic, monitors proposed actions with an automaton, and supports replanning through violation feedback. Its operation depends on reliable proposition truth values and a human able to verify the formal constraints.
B. Overview
- The overview composes natural-language safety constraints into LTL formulas stored as an automaton, which monitors and prunes agent actions and supports violation queries for replanning.The query system generates responses from environment knowledge and explanations of constraint violations.Source: B. Overview · p. 3
C. Identifying Safety Constraints
- Constraint identification uses a two-phase workflow: translate English constraints into LTL, then have a human designer verify the formulas.The modular translator grounds referring expressions in a predefined vocabulary, translates lifted utterances, and assembles the grounded formulas; it relies on in-context learning without fine-tuning and supports predicates beyond navigation.Source: C. Identifying Safety Constraints · p. 3
D. Enforcing Safety Constraints
- The monitor progresses the action trajectory through the constraint automaton and rejects sequences that violate state transitions or cannot reach an accepting state.The monitoring guarantee assumes perfect proposition truth-value functions and a correct transition model; the chip can stop execution when an unsafe state is detected.Source: D. Enforcing Safety Constraints · p. 3, E. Reprompting with Large Language… · p. 3
E. Reprompting with Large Language Models
- Reprompting masks an unsafe action and adds a violation reason to the prompt so the LLM can regenerate its plan with updated context.The example shows successive feedback enforcing the order bedroom, living room, bathroom, then toilet.Source: E. Reprompting with Large Language… · p. 4
F. Queryable Safety Constraints
The query system checks proposed actions against safety constraints and gives the agent explanations to support reprompting. Experiments evaluate constraint reasoning in VirtualHome tasks.
F. Queryable Safety Constraints
- The query system masks unsafe actions and returns violation explanations as feedback for the agent’s next decision.It is prompted only after the monitor detects a violation; the automaton determines action validity, leaving the LLM to explain the reason.Source: F. Queryable Safety Constraints · p. 4
IV. EXPERIMENTS
- The evaluation tests whether the safety chip reduces constraint violations without reducing task completion relative to agents without it.The comparison includes a baseline that receives goal specifications and constraints together.Source: §IV · pp. 4–5
A. VirtualHome Environment & Tasks
- VirtualHome experiments use 20 household tasks across two environments, with five satisfiable constraints per task.Tasks have in-context examples containing the ground-truth action sequence, with only the goal specification paraphrased; constraints follow avoidance and trigger patterns.Source: A. VirtualHome Environment & Tasks · p. 5
B. LLM Agents
The experiments compare a base agent, an agent given natural-language constraints, and one equipped with the safety chip. They vary constraint difficulty and report safety and task-completion outcomes in simulation and a robot demonstration.
B. LLM Agents
- The Safety Chip agent tracks states, prunes unsafe actions, and uses violation messages to regenerate actions; the NL Constraints agent reasons internally.The base model provides environmental information and available actions, then generates and grounds one action at a time.Source: B. LLM Agents · p. 5
C. Experimental Setup
- Experiments vary the number of constraints from one to five and measure task success and safety in Four Room and Mobile Manipulation.Four Room uses navigational predicates; Mobile Manipulation adds six object-state and relationship predicates and averages 105 available objects per environment.Source: C. Experimental Setup · p. 5
D. Results
- 100% safety rate with expert-verified LTL formulas in both experiments; Safety Chip significantly outperformed the baselines, especially with more constraints.Without expert verification, it still had a large margin over the baselines, though success rate was affected by mistranslated constraints.Source: D. Results · p. 5, Figure 4 · p. 5, Table I · p. 6
V. ROBOT DEMO
- The robot demonstration doubles the maximum number of constraints per task and compares the system on a Spot robot with two baselines.GPT-4 is used for all prompting tasks.Source: B. LLM Agents · p. 5, §V · p. 6
A. Experimental Setup
In the Spot-robot indoor setup, the Safety Chip achieved 100% safety without reducing success, while the two baselines’ safety rates fell as constraints increased. The paper concludes that validated LTL is more reliable for safety than end-to-end approaches and identifies several directions for future work.
A. Experimental Setup
- The real-robot setup used 23 objects and landmarks, two mobile-manipulation tasks, and ten constraints per task, increasing the number applied progressively.The constraints covered six avoidance and trigger patterns; the evaluation assumed expert-verified LTL without mistranslation and no mechanical failure.Source: A. Experimental Setup · p. 6
B. Results
- With LTL experts, the Safety Chip achieved 100% safety rate without impacting success; above five constraints, NL Constraints reached 0% and Code as Policies 40%.Below five constraints, NL Constraints achieved 75% and Code as Policies 70%.Source: B. Results · p. 6
- In three tests with task-conflicting safety constraints, NL Constraints and Code as Policies attempted the task and violated safety, while the Safety Chip aborted before any unsafe state.Source: B. Results · p. 6
VI. CONCLUSION & FUTURE WORK
- The conclusion characterizes safety constraints as increasingly challenging and distracting for LLM agents without external help, and validated formal languages such as LTL as more reliable and interpretable for safety.Source: §VI · p. 6
- The authors suggest that separating safety constraints from different abstraction levels may help LLM agents focus on reasoning, and that the Safety Chip may apply beyond LLM agents.Source: §VI · p. 6
- Future work includes interactive constraint confirmation, richer natural-language representations from automata and state transitions, and neural truth-value functions with open-vocabulary propositions.Source: §VI · p. 6
APPENDIX
The appendix walks through a Safety Chip example with ten simultaneous constraints, showing how natural-language constraints are translated into LTL, combined for automaton monitoring, and used to prompt plan corrections.
A. Complete Safety Chip Example
- The example applies ten constraints to a book-delivery task, including prohibitions, ordering requirements, and future-visit obligations.Source: A. Complete Safety Chip Example · p. 9
- The system translates each constraint into LTL, combines the formulas with AND, and converts them into an automaton for monitoring and pruning.The automaton also generates state changes required by the query system.Source: A. Complete Safety Chip Example · p. 9
- The execution example shows reprompting errors correcting violations of ordering, prerequisite, and future-action constraints before the plan can finish.The displayed corrections redirect the agent to perform the required actions rather than continue or stop prematurely.Source: A. Complete Safety Chip Example · p. 10
B. NL to LTL Translation
The NL-to-LTL translator uses a three-stage pipeline: it extracts referring expressions, grounds them to predefined objects, then translates a placeholder-substituted utterance and restores the grounded objects.
B. NL to LTL Translation
- The translation pipeline extracts referring expressions, grounds them to predefined objects by cosine similarity, and translates the lifted utterance before replacing placeholders with grounded objects.Source: B. NL to LTL Translation · p. 10
1) Referring Expression Recognition:
- Referring-expression recognition prompts the model to repeat exact utterance strings that may name objects or locations.Examples include extracting “mail,” “livingroom,” and “mail room” from a task instruction.Source: 1) Referring Expression Recognition · pp. 10–11
- The examples show that recognition preserves multiword or descriptive references, including “big red room,” “yellow region,” and “Cutler Majestic Theater.”Source: 1) Referring Expression Recognition · pp. 10–11
2) Lifted Translation:
The lifted-translation prompt maps English utterances to LTL using a specified set of propositions, operators, and action predicates. The examples cover prohibitions, ordering, and temporal obligations.
2) Lifted Translation:
- The translation prompt defines LTL as a temporal-specification language and lists propositions, operators, and predicates such as agent-at and is-grabbed.Source: 2) Lifted Translation · p. 11
- The examples encode prohibitions with globally-not formulas, such as never visiting A or never grabbing A.Source: 2) Lifted Translation · p. 11
- Weak-until examples express prerequisites, such as not going to B until visiting A or picking up A before going to B.Source: 2) Lifted Translation · p. 11
- Other examples encode triggered future obligations and immediate next-step requirements using the finally and next operators.The examples include visiting B after picking up A and closing B immediately upon entering A.Source: 2) Lifted Translation · p. 11
- The examples also encode limits on repeated visits or actions, including visiting A only once and opening A at most twice.Source: 2) Lifted Translation · p. 11
C. Implementation Details of Base LLM Agent
The base LLM prompt specifies the task, available actions, rooms, and safety constraints, while Code as Policies prompts generate executable scripts and resolve object references.
C. Implementation Details of Base LLM Agent
- The base-agent prompt asks for a high-level household-task plan using allowed actions, one action per step, constraint-error replanning, and DONE to mark completion.The listed actions and rooms are supplied in the prompt; object lists are appended for the robot-demo and Mobile Manipulation tasks in VirtualHome.Source: C. Implementation Details of Base LLM… · p. 12
D. Implementation Details of Code as Policies
- Code as Policies uses separate prompts for executable Python code generation and parsing object references into environment objects.Generated scripts call navigation, manipulation, and object-resolution functions.Source: D. Implementation Details of Code as… · pp. 12–13
- The object-resolution prompt maps phrases such as “wooden brown bookshelf” and “telephone” to available objects such as “book shelf” and “phone.”The prompt provides an object list alongside example phrase-to-object mappings.Source: D. Implementation Details of Code as… · p. 13
- Constraint instructions appear as comments in the prompt, with examples encoding required visit order, later visits, and prohibited locations.Examples include avoiding a bookshelf while retrieving mail and visiting a couch before picking up a book.Source: D. Implementation Details of Code as… · pp. 12–13
E. Example VirtualHome tasks and Constraints
The examples show how VirtualHome tasks pair natural-language instructions with step-by-step programs and temporal safety constraints. They cover both Four Room and Mobile Manipulation tasks.
E. Example VirtualHome tasks and Constraints
- VirtualHome in-context examples combine a high-level description, low-level task details, and an execution program that is converted into templated natural language.A program step such as putting salmon in the fridge is rendered as “put salmon in fridge,” in a format similar to the robot-demo example.Source: E. Example VirtualHome tasks and… · p. 13
- The Four Room example pairs “Put salmon in Fridge” with a step sequence and constraints on visit order, future visits, and visit counts.The constraints require entering the bathroom before the kitchen, later visiting the living room after entering the kitchen, and limit kitchen and bedroom visits.Source: E. Example VirtualHome tasks and… · p. 13
- The Mobile Manipulation example pairs “Walk to toilet” with a route through the bathroom and constraints on visit order, object actions, and bathroom visits.The constraints include picking up an apple before placing it in the fridge and closing the fridge later if it is opened.Source: E. Example VirtualHome tasks and… · p. 13
F. Generating Explanation for Re-prompting
For re-prompting, the system supplies the violated action, natural-language constraints, and proposition-level state changes to an LLM. Its response explains why the action violates a constraint.
F. Generating Explanation for Re-prompting
- After detecting a violation, the system constructs an explanation prompt using the natural-language constraints and the unsafe action.The example extends this prompt with a partial valid trajectory and proposition-level state changes derived from the automaton.Source: F. Generating Explanation for… · pp. 13–14
- The explanation prompt tracks each constraint proposition’s truth value across action-state changes and labels each step Safe or Violated.The truth values are concatenated with AND operators; the prompt also clarifies that ! denotes False.Source: F. Generating Explanation for… · pp. 14–15
- The example explanation says grabbing the book is disallowed because the coffee machine has not yet been turned on.The violated temporal constraint requires turning on the coffee machine before picking up the book.Source: F. Generating Explanation for… · p. 15