RoboRacer Arena: Specification-Driven Track Construction for Autonomous Racing

Mihaela-Larisa Clement; Agnes Poks; Ezio Bartocci

cs.ROAug 24, 2026

RoboRacer Arena: Specification-Driven Track Construction for Autonomous Racing

Authors: Mihaela-Larisa Clement, Agnes Poks, Ezio Bartocci

Organizations: 1TU Wien, Vienna, Austria. · 2AIT Austrian Institute of Technology, Vienna, Austria.

Abstract

Learning-based autonomous racing relies on diverse training environments, yet constructing a new track requires geometric design, validation, and simulation-asset generation. This coupling makes track geometry difficult to vary systematically during policy training and evaluation. To address this limitation, we present RoboRacer Arena, a specification-driven pipeline that exposes track geometry as an explicit experimental variable. Starting from natural-language requirements, a seeded coverage-guided constructor generates closed-loop layouts, validates the exported occupancy maps, and automatically builds the corresponding Isaac Sim environments. The same interface admits recorded maps and scaled circuits, yielding an initial reference collection of 130 tracks. Across our evaluation, RoboRacer Arena achieves the highest valid-map generation rate among the tested construction procedures under their respective computational budgets and converts eight benchmark maps into simulation assets in less than 2.5 seconds each. We further reconstruct the RoboRacer vehicle as a CAD and USD asset and use it for parallel residual-policy training and deployment on the physical platform. In physical experiments, policies trained with RoboRacer Arena complete ten consecutive laps at command settings up to four times the nominal training speed. These results demonstrate that explicit track requirements can be connected to validated simulation assets and physical evaluation within a reproducible autonomous-racing workflow.

Figures & tables

Panel of Figure 1 — Fig. 1 : Overview of RoboRacer Arena, from track specification and map import to parallel policy training and physical deployment. The vehicle asset is reconstructed from component scans and shared across the simulation and physical workflows.

Panel of Figure 2 — Fig. 2 : Overview of the specification-guided construction pipeline. Feasible requests guide grid-based construction and smoothing, after which the exported occupancy map is validated before USD generation. Invalid maps return to structure sampling, whereas infeasible requests terminate before search.

Parameter	Value	Unit
Wheelbase	0.3275	[m]
Track width	0.265	[m]
Mass	4.885	[kg]
Yaw inertia	0.089	[kg m 2 ]
CoM to front axle	0.1753	[m]
CoM to rear axle	0.1522	[m]

TABLE I : Physical RoboRacer parameters and operating statistics on GP Track A. CoM denotes centre of mass,

p_{99}

the 99th percentile, and dashes unreported statistics.

Model	Parse	Checks	Time [s]	VRAM [GB]
Gemma 4 31B	12/12	51/51	9.3	19.0
Qwen 3.6 35B	12/12	51/51	12.5	23.9
GPT-OSS 120B	12/12	51/51	3.7	65.4
Mistral 7B	12/12	45/51	0.8	4.4

TABLE II : Constraint preservation across local language models. Parse comprises 12 requests, whereas Checks comprises 17 requests repeated three times. Runtime is reported per call with the model resident in memory.

Constructor	Trials	Built	Valid	Time
Coverage-guided	30	21	21	3.7
DE optimiser	30	27	15	5.1
Random sampling	30	30	11	11.2

TABLE III : Comparison of map construction procedures over 30 trials per method. All outputs are evaluated by the same raster validator, while search budgets follow each procedure’s native stopping rule. Runtime is reported in seconds per trial.

Panel of Figure 6 — Fig. 3 : Four layouts generated from the same request: a 45-m counter-clockwise lap of 1.6-m width, with three hairpins and one chicane in a $20\times 15$ -m hall.

Map	Length [m]	Raster [MP]	Build [s]	USD [MB]
RoboRacer GP	81.26	0.13	1.18	7.8
Norisring	229.36	2.56	1.49	21.6
Interlagos	430.40	3.02	1.68	40.5
Monaco	332.86	3.04	1.60	31.3
Zandvoort	431.58	3.48	1.75	40.6
Silverstone	588.51	7.40	2.16	55.4

TABLE IV : Map-to-USD conversion across eight inputs. Length denotes centreline lap length at simulation scale, with imported circuits scaled to 1:10. Runtime uses one CPU core and excludes simulator startup.

Metric	F1TENTH Gym	AutoDRIVE	RoboRacer Arena
Parallel environments	24	20	256
Aggregate steps/s	$1090$	374	$7262$
Steps per env. [Hz]	45.4	18.7	28.4
Track collection	23 official	6 predefined	130 reference
Track generation	–	–	$\checkmark$
Occupancy-map input	$\checkmark$	–	$\checkmark$

TABLE V : Comparison of training throughput and supported track inputs at the tested batch sizes. The official F1TENTH track collection is distributed separately [ 16 ] .

Fig. 4 : Spatial distribution of tracking error on clean laps. Colour identifies the lower-error policy in each section, while crosses mark collisions of the Gym-trained policy. The RoboRacer Arena-trained policy completes all five runs without a crash.

Fig. 5 : Physical trajectories under increasing speed commands. The Gym-trained policy is shown above the RoboRacer Arena-trained policy. Columns include the 1.5-m/s baselines, colour denotes achieved speed capped at the pooled 99th percentile of clean-lap speed, and red marks collision approaches.

Fig. 6 : Tracking and speed response across non-baseline commands. The RoboRacer Arena-trained policy maintains lower mean lateral error throughout the sweep. Speed ratio compares achieved speed with the local raceline target, and the dashed line denotes a ratio of one. Table VI additionally includes the baseline runs.

Training platform	Metric	$\boldsymbol{\mu}$	$\boldsymbol{\sigma}$	Min	Max
F1TENTH Gym	$E$ [m]	0.148	0.062	0.062	0.196
	$R$ [ $-$ ]	1.063	0.304	0.612	1.257
RoboRacer Arena	$E$ [m]	0.093	0.014	0.069	0.104
	$R$ [ $-$ ]	0.832	0.134	0.595	0.906

TABLE VI : Physical deployment results across all nine runs, including baselines. Statistics weight run means equally.

E

denotes mean absolute lateral error,

R

mean speed ratio, and

\sigma

sample standard deviation; Min and Max are extrema of run means.

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Autonomous Driving Benchmarks Robot Trajectory Tracking Trajectory Optimization Trajectory Generation Autonomous Racing Vehicle Dynamics+1

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We present a modular framework to benchmark new and existing methods for trajectory planning and control in high-acceleration maneuvers that push autonomous driving to the limits. Our framework includes time-optimal raceline generation, online time-optimal velocity replanning, geometric path tracking controllers, and a new model-structured neural network (MS-NN) to learn the inverse dynamics for steering control. We deploy our framework on a 1:10-scale RoboRacer platform, using two circuits. Through several ablations with cautious and aggressive racelines, we study the performance of single modules and their combinations. We show that our MS-NN significantly improves tracking accuracy, decreases steering oscillations, and is physically interpretable. Moreover, online velocity replanning improves lap times by compensating for execution errors, and enables the vehicle to safely reach higher speeds and accelerations. To support future research, our code, datasets, videos and results are publicly available at https://roboracer-benchmark.github.io/planning_control_benchmark/.

Abstract

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