Natural-Language to Nav2: Operational Policies for Costmap-Based Navigation

Jiyeong Chae; Hyunkyo Seo; Heonjae Lee; Kyung-Joon Park

cs.ROOct 23, 2026

Natural-Language to Nav2: Operational Policies for Costmap-Based Navigation

Authors: Jiyeong Chae, Hyunkyo Seo, Heonjae Lee, Kyung-Joon Park

Organizations: Daegu Gyeongbuk Institute of Science and Technology (DGIST) 333 Techno Jungang-daero, Hyeonpung-eup, Dalseong-gun, Daegu, 42988, Republic of Korea

Abstract

Autonomous mobile robots support material transport in warehouses and industrial facilities, where navigation must adapt to changing operating conditions. Sensor observations reveal physical obstacles, but do not by themselves specify temporary access rules, route preferences, or exceptions known to operators. This paper presents a natural-language policy interface that brings this knowledge into ROS 2 Nav2. A local large language model converts instructions into JSON policies specifying destinations, spatial constraints, and temporal or event conditions. Deterministic checks govern mission acceptance, and three costmap adapters project accepted policies into the existing navigation stack without changing its planner or controller. On 150 researcher-authored English commands, four local models produce 107-142 correct accepted policies, compared with 44 for a rule-based parser, with 3-20 incorrect acceptances per model. Fault injection records rejection of eight endpoint/JSON faults and 29 validator-stage faults. Navigation experiments use correct task policies to evaluate keepouts, zone costs, person-dependent regions, and event-conditioned passage in simulation and on a TurtleBot3. For Tasks 1-3, mean travel times are 18.4-24.5% lower in simulation and 23.1-35.0% lower on the physical testbed than with the baseline lacking those policies. A native KeepoutFilter comparison and two-robot demonstrations examine basic keepout behavior and shared policy projection. The interface connects operator instructions to explicit navigation policies while retaining Nav2's planning and control components.

Figures & tables

Figure 1: The policy interface has three stages. The LLM receives an instruction and registered map labels. Parsing, validation, and execution-context checks govern mission acceptance. Accepted state supplies waypoint goals; policy state and separately maintained restrictions determine the cost contributions. Standard sensor layers, inflation, NavFn, and DWB remain in the navigation stack. The example shows selected policy fields.

Aspect	Native KeepoutFilter [ 6 ]	Proposed policy interface
Policy validation	Filter metadata checks	Whole-candidate schema, conflict, and context checks
Mission replacement	Outside filter scope	Replace mission snapshot; preserve separately managed restrictions
Restriction lifetime	Retained mask; externally updated	Mission-scoped, permanent, and timed restrictions
Event handling	External mask updates	Event-conditioned permissions with priorities

Table 1: Keepout filtering and policy management. The native column covers KeepoutFilter; Nav2 task orchestration lies outside that component.

Fields	Interpretation and execution
plan , via_zones	Ordered destinations and required transit zones. The bridge resolves transit waypoints and builds the goal sequence.
exclusions	Zones to keep closed, with optional duration or absolute time interval.
weights	Integer zone costs for traversal preferences.
dynamic_object_rules	Object class and avoidance radius used around detections.
conditional_rules	Zone actions selected by event/state conditions and priority.

Table 2: The JSON policy contract. A candidate may also explicitly reject an instruction using decision and reason_code .

Method	Correct	Incorrect	95% CI (%)	Mean latency
Rule-based parser	44	101	[22.6, 37.1]	0.175 ms
Llama 3.1-8B	107	20	[63.6, 78.0]	2.533 s
Qwen2.5-7B	122	10	[74.3, 86.8]	1.694 s
Qwen3-8B	141	7	[89.0, 96.8]	1.800 s
Qwen3.5-9B	142	3	[89.8, 97.3]	3.011 s

Table 3: Policy conversion on 150 commands per method. Correct and incorrect columns both count accepted policies. The nominal 95% Wilson interval applies to correct acceptance.

Figure 2: Post-validation exact-match accuracy across five phrasing categories, with 30 commands per category. Exact counts and the complete acceptance/rejection breakdown are provided in the Supplementary Material .

Fault class	Injected	Rejected	Stage
Endpoint/JSON	8	8	Before validation
Schema/type	8	8	Validator
Domain/range	11	11	Validator
Time/conflict	10	10	Validator

Table 4: Injected faults and their containment stage. All 37 faults preserve the prior mission state and produce no new policy projection. Six admissible controls are evaluated separately.

Figure 3: Simulation testbed. Aisles are numbered from top to bottom. Tasks 1 and 4 connect WP1 to WP2; Task 2 follows WP5–WP6–WP4–WP3–WP5; Task 3 connects WP7 to WP8. Arrows indicate schematic mission segments.

Figure 4: Physical testbed. Task 1 connects WP1 to WP2, Task 2 WP3 to WP4, Task 3 WP5 to WP6, and Task 4 WP4 to WP1. The map shows the waypoint connections for the four tasks.

Figure 5: Representative simulation Task 1 trajectories. Supplying the closure before navigation changes the initial route and avoids the approach to the blocked aisle.

Figure 6: Task 3 in the physical maze. A detected person activates the policy-defined object region in the costmap used for planning.

Figure 7: Representative physical Task 4 trajectories. The active passage permission allows a more direct route within the same policy-enabled stack. Priority-Off and Priority-On differ only in this policy permission.

Task	Metric	Baseline	Policy	95% CI: Baseline $-$ Policy
1	Time (s)	$114.009\pm 0.721$	$86.125\pm 0.230$	[27.356, 28.412]
1	Distance (m)	$48.572\pm 0.108$	$37.245\pm 0.108$	[11.226, 11.428]
2	Time (s)	$311.369\pm 1.444$	$238.751\pm 3.643$	[69.912, 75.324]
2	Distance (m)	$126.152\pm 0.180$	$96.003\pm 0.177$	[29.981, 30.317]
3	Time (s)	$142.049\pm 0.315$	$115.872\pm 0.790$	[25.590, 26.764]
3	Distance (m)	$61.185\pm 0.140$	$49.527\pm 0.340$	[11.405, 11.911]

Table 5: Simulation navigation: travel time and distance (mean

\pm

sample SD,

n=10

per condition). Intervals are approximate 95% Welch confidence intervals for the indicated difference, in the metric’s units.

Task	Metric	Baseline	Policy	95% CI: Baseline $-$ Policy
1	Time (s)	$62.179\pm 1.322$	$40.390\pm 1.213$	[20.596, 22.982]
1	Distance (m)	$11.993\pm 0.210$	$8.138\pm 0.190$	[3.667, 4.043]
2	Time (s)	$55.849\pm 0.687$	$42.959\pm 1.079$	[12.029, 13.751]
2	Distance (m)	$10.850\pm 0.080$	$8.303\pm 0.156$	[2.428, 2.666]
3	Time (s)	$42.109\pm 2.073$	$32.160\pm 1.153$	[8.341, 11.557]
3	Distance (m)	$8.020\pm 0.352$	$6.394\pm 0.216$	[1.348, 1.904]

Table 6: Physical-testbed navigation: travel time and distance (mean

\pm

sample SD,

n=10

per condition). Intervals are approximate 95% Welch confidence intervals for the indicated difference, in the metric’s units.

Method	Completed	Distance (m)	Time (s)
Native KeepoutFilter	5/5	$37.339\pm 0.103$	$86.430\pm 0.236$
Custom KeepoutLayer	5/5	$37.300\pm 0.131$	$86.351\pm 0.329$

Table 7: Direct keepout control, with five restarted runs per condition. Values are mean

\pm

sample SD. Travel time starts at first detected motion and ends at the waypoint-action completion result.

Figure 8: Shared policy projection in the physical two-robot demonstration. Matching policy-induced regions are visible in both costmaps. Each robot retains its own sensor layers, localization, planner, and controller.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Raw	Post-V	Accuracy (%)	95% CI (%)	Latency
Rule-based parser	44/150	44/150	29.3	[22.6, 37.1]	0.175 ms
Llama 3.1-8B	106/150	107/150	71.3	[63.6, 78.0]	2.533 s
Qwen2.5-7B	122/150	122/150	81.3	[74.3, 86.8]	1.694 s
Qwen3-8B	141/150	141/150	94.0	[89.0, 96.8]	1.800 s
Qwen3.5-9B	142/150	142/150	94.7	[89.8, 97.3]	3.011 s

Appendix

Table S1: Policy-conversion results on 150 commands. Raw and Post-V count policies matching the reference before and after validation, respectively. The 95% Wilson interval is nominal and applies to Post-V. Latency concerns candidate generation only.

Method	L0	L1	L2	L3	L4
Rule-based parser	60.0	20.0	30.0	26.7	10.0
Llama 3.1-8B	83.3	80.0	80.0	53.3	60.0
Qwen2.5-7B	83.3	86.7	83.3	70.0	83.3
Qwen3-8B	100.0	93.3	100.0	90.0	86.7
Qwen3.5-9B	100.0	93.3	100.0	86.7	93.3

Appendix

Table S2: Post-validation exact-match accuracy (%) by phrasing level, with 30 commands per level.

	Accepted		Rejected by
Method	Correct	Incorrect	Interpreter	Parsing	Validator
Rule-based parser	44	101	5	0	0
Llama 3.1-8B	107	20	0	0	23
Qwen2.5-7B	122	10	3	0	15
Qwen3-8B	141	7	1	0	1
Qwen3.5-9B	142	3	4	0	1

Appendix

Table S3: Mutually exclusive processing outcomes, each row summing to 150. Interpreter rejection denotes an explicit model or parser rejection.

Method	C1	C2	C3	C4	C5	Recognized	Blocked
Rule-based parser	20	10	0	40	50	24/100	24/100
Llama 3.1-8B	55	55	90	15	10	45/100	96/100
Qwen2.5-7B	85	90	90	55	65	77/100	97/100
Qwen3-8B	100	90	100	85	85	92/100	99/100
Qwen3.5-9B	100	100	100	100	100	100/100	100/100

Appendix

Table S4: Contradiction recognition by category (%, 20 commands each) and overall counts out of 100. Recognized denotes explicit contradiction recognition; Blocked denotes rejection at the policy-acceptance boundary.

Method	Recognition	Boundary containment
Rule-based parser	[16.7, 33.2]	[16.7, 33.2]
Llama 3.1-8B	[35.6, 54.8]	[90.2, 98.4]
Qwen2.5-7B	[67.8, 84.2]	[91.5, 99.0]
Qwen3-8B	[85.0, 95.9]	[94.6, 99.8]
Qwen3.5-9B	[96.3, 100.0]	[96.3, 100.0]

Appendix

Table S5: Nominal 95% Wilson intervals (%) for the 100-command contradiction results. The same command-level and shared-category limitations apply as in the conversion evaluation.

Case	Input and context	Expected behavior
T1	Valid policy	Accept constraints and construct the requested goal sequence.
T2	New mission	Replace the snapshot without inheriting omitted ordinary constraints.
T3a	Exclusion and required transit in one policy	Reject the update and preserve the preceding mission.
T3b	Required transit under a timed keepout	Reject without silently deleting required transit.
T3c	Required transit under a permanent keepout	Reject and retain the preceding mission.
T3d	Required transit after expiry	Accept the candidate with transit retained.

Appendix

Table S6: Node-level checks on the tested implementation. All eight expected outcomes are observed.

Figure S1 : Expiry of a Zone A keepout during one simulation run. The destination remains active as the policy contribution is removed and Nav2 selects a route through the released region.

Task	Metric	Baseline	Policy	95% CI: Baseline $-$ Policy
1	Time (s)	$114.009\pm 0.721$	$86.125\pm 0.230$	[27.356, 28.412]
1	Distance (m)	$48.572\pm 0.108$	$37.245\pm 0.108$	[11.226, 11.428]
2	Time (s)	$311.369\pm 1.444$	$238.751\pm 3.643$	[69.912, 75.324]
2	Distance (m)	$126.152\pm 0.180$	$96.003\pm 0.177$	[29.981, 30.317]
3	Time (s)	$142.049\pm 0.315$	$115.872\pm 0.790$	[25.590, 26.764]
3	Distance (m)	$61.185\pm 0.140$	$49.527\pm 0.340$	[11.405, 11.911]

Appendix

Table S7: Simulation navigation: mean

\pm

sample SD,

n=10

per condition. Approximate 95% Welch intervals are for the difference between conditions.

Task	Metric	Baseline	Policy	95% CI: Baseline $-$ Policy
1	Time (s)	$62.179\pm 1.322$	$40.390\pm 1.213$	[20.596, 22.982]
1	Distance (m)	$11.993\pm 0.210$	$8.138\pm 0.190$	[3.667, 4.043]
2	Time (s)	$55.849\pm 0.687$	$42.959\pm 1.079$	[12.029, 13.751]
2	Distance (m)	$10.850\pm 0.080$	$8.303\pm 0.156$	[2.428, 2.666]
3	Time (s)	$42.109\pm 2.073$	$32.160\pm 1.153$	[8.341, 11.557]
3	Distance (m)	$8.020\pm 0.352$	$6.394\pm 0.216$	[1.348, 1.904]

Appendix

Table S8: Physical navigation: mean

\pm

sample SD,

n=10

per condition. Approximate 95% Welch intervals are for the difference between conditions.

Task	Metric	Difference	$t$	Degrees of freedom	$p$
Simulation
1	Time (s)	27.884	116.5134	10.8129	$4.165\times 10^{-18}$
1	Distance (m)	11.327	234.5180	18.0000	$7.968\times 10^{-33}$
2	Time (s)	72.618	58.5999	11.7599	$7.129\times 10^{-16}$
2	Distance (m)	30.149	377.6629	17.9949	$1.535\times 10^{-36}$
3	Time (s)	26.177	97.3314	11.7912	$1.689\times 10^{-18}$

Appendix

Table S9: Approximate two-sided Welch test statistics. Differences are Baseline

-

Policy for Tasks 1–3 and Priority-Off

-

Priority-On for Task 4. No multiple-comparison adjustment is applied.

Figure S2 : Physical Task 1. The policy-informed route avoids the declared closure.

Figure S3 : Simulation Task 2. Both panels show the complete multi-waypoint mission.

Figure S4 : Physical Task 2. Zone costs change the selected corridor.

Figure S5 : Simulation Task 3 with the person-dependent cost region.

Figure S6 : Physical Task 3. The policy changes the route taken through the maze.

Figure S7 : Simulation Task 4. This event-permission comparison is distinct from Baseline versus Policy.

Figure S8 : Shared policy projection on two simulated robots. The policy contribution agrees even when local sensor information and planned paths differ.

Model	GPU $\Delta$ (pp)	VRAM $\Delta$ (MiB)	CPU (%)	Host RSS (GiB)
Qwen2.5-7B	54.7	4547	16.8	0.590
Qwen3-8B	58.3	5319	15.6	0.609
Qwen3.5-9B	58.7	6438	17.4	1.005
Llama 3.1-8B	61.8	5043	12.8	0.567

Appendix

Table S10: Local-inference resources. GPU change is in percentage points (pp). Host memory is RSS.

Type	Llama	Qwen2.5	Qwen3	Qwen3.5
D: destination or visit-order error	7	2	2	1
R: requested keepout or person-avoidance omission	2	1	2	1
N: cost omission, target, or role error	3	2	1	0
E: event condition, action, or priority error	0	0	0	0
A: unrequested transit or avoidance constraint	14	7	3	1
Distinct incorrect acceptances	20	10	7	3

Appendix

Table S11: Accepted-error types in stored LLM outputs. Columns follow the model order used in Table 3 ; types overlap.

Command	Policy difference	Potential operational effect
N01-L1-V2	Adds person avoidance with radius 1.5 m.	Additional avoidance may alter route choice.
N02-L1-V2	Keeps WP7 but omits the required preceding WP2 stop.	The requested pickup or visit may be skipped.
N09-L3-V2	Omits the C keepout while retaining transit B, cost 40 in A, and person radius 2 m.	Zone C may remain available to the planner.

Appendix

Table S12: All three Qwen3.5-9B incorrect acceptances and their potential effects, inferred from the recorded policy content.

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Department of Robotics and Mechatronics Engineering, DGIST, Daegu, Republic of Korea

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Human-readable maps provide an intuitive interface for specifying robot destinations, but connecting their schematic geometry to egocentric observations remains challenging. We present NaViRRator, a framework that translates user-specified start and goal locations on such maps into navigation instructions for a pretrained vision-and-language navigation (VLN) policy. Its core method, RouteScribe, separates route inference from verbalization by first generating an explicit route scaffold in map-image coordinates, which a pretrained vision-language model (VLM) converts into a navigation instruction. We construct the scaffold with start--goal line conditional flow matching (SGL-CFM), which deforms a straight start--goal waypoint sequence into a map-conditioned route. During execution, the VLN policy receives only the instruction and egocentric observations, while the map and scaffold remain upstream, allowing executor replacement without retraining the map-to-language modules. Real-world experiments show higher success rates and success weighted by path length (SPL) than direct map-to-instruction generation, A*-based scaffolding, and Gaussian-source conditional flow matching. Qualitative results further show clearer salient turns and better preservation of the intended maneuver sequence, supporting route-grounded language as a modular interface between human-readable maps and pretrained navigation policies.

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