RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
Authors: Matteo El-Hariry
Antoine Richard
Ricard Marsal
Luis Felipe Wolf Batista
Matthieu Geist
Cédric Pradalier
Miguel Olivares-Mendez
Venue: Accepted by TMLR ()

Abstract: Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specific autonomous robots, existing frameworks and benchmarks are often constrained to unique platforms, limiting generalization and fair comparisons across different mobility systems. In this paper, we present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments. Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real- world experiments with multiple robots, including a satellite robotic simulator, an unmanned surface vessel, and a wheeled ground vehicle; (3) the release of the first open-source API for deploying IsaacLab-trained policies to real robots, enabling lightweight inference and rapid field validation; and (4) uniform tasks and metrics for cross-medium evaluation, through a unified evaluation testbed to assess performance of navigation tasks in diverse operational conditions (aquatic, terrestrial and space). By ensuring consistency between simulation and real-world deployment, RoboRAN lowers the barrier to developing adaptable RL-based navigation strategies. Its modular design enables straightforward integration of new robots and tasks through predefined templates, fostering reproducibility and extension to diverse domains. To support the community, we release RoboRAN as open-source.

Published in Transactions on Machine Learning Research (11/2025)
RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
Matteo El-Hariry'
motteo. caateo.elharinySunn.kkk
Space Robotics Reseorch Group, SnT University of Luzembourg
Antoine Richard"
antoine.richarrdSuume
Space Robolics Reseorch Croup, Su/T
University of Luzembourg
Ricard M. Castan"
macrd.
Space Robotics Research Group, Sn'l'
University of Lazzeboorr
Luis W. Batista"
batista @gotchh edu
Georgia Tech- Purope
IRL2958 GT-CNRS, Melz, France
Cedric Pradalier
Georgia Tech-Earee Europe
IRL2958 GT GT-CNRS, Metz, France
Matthieu Geist
malthicuu@earllsspciess.yyy
Earth Species Project
Miguel Olivares-Mendez
migeel.diiaremeendee@w...l
Space Robotics Reseorch Group, SnT University of Lazembourg
Reviewed on OpenReview: https: // openreview. net/ formm? id= OuDbhLeMj9
Abstract
Autonomous robots must navigate and operate in diverse environments, from terrestrial and squatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specific autonomous robots, existing frameworks and benchmarks are often constrained to unique platforms, limiting generalization and fair comparisons across different mobility systems. In this paper, we present multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments. Our work presents four key contributions: (1) scalable and modular framework, facilitating scamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real- world experiments with multiple robots, including a satellite robotic simulator, an unmanned surface vessel, and a wheeled ground vehicle; (3) the release of the first open-source APl for deploying Isaac Lab-tre babbtrrined policies to real robots, enabling lightweight inference and rapid field validation; and (4) uniform tasks and metrics for cross-medium evaluation, through unified evaluation testbed to assess performance of navigation tasks in diverse operational conditions (aquatic, terrestrial and space). By ensuring consistency between simulation and real-world deployment, RoboRAN lowers the barrier to developing adaptable RL-based
Equul contribution

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Published in Transactions on Machine Learning Research (11/2025)
Figure 1: RoboRAN supports casy development of RL-based navigation tasks across a diverse set of robots. The five robots shown are Kingfisher, Floating Platform, Turtlebot2, Leatherback, and Jetbot. All have been implemented simulation, while the first three have also been evaluated and demonstrated in real-world environments.
navigation strategies. Its modular design enables straightforward integration of new robots and tasks through predefined templates, fostering reproducibility and extension to diverse domains. To support the community, release RoboRAN as open-source'.
1 Introduction
One of the main goals of robotics research is to develop autonomous agents capable of navigating and exccuting tasks in any environment and medium, from terrestrial rovers and aquatic vessels to acrial drones and spacecraft. While Reinforcement Learning (RL) has shown promise in training such agents (Taheri et al., 2024; Wisniewski et al., 2024; Tezerjani et al., 2024; Surmann et al., 2020), more often than not, the capabilities of these agents are only illustrated on single robot and on single task. Existing benchmarks are often restricted to specific robot types and environment settings, limiting generalization and rross-domain comparisons. Although this approach is well suited to grow domain-specifie knowledge, makes it hard to see how method would transfer to different system. Hence, to scale RL for robotics, this paradigm must evolve toward more generalizable frameworks, enabling the development of universal solutions and their efficient adaptation to specific systems.
the literature, we can find many specialized RL frameworks, be used to control boats (Batista et al., 2024), satellites (El-Hariry al., 2024; 2023), or ground rovers (Perille al., 2020), but there has been little effort evaluate these systems under common structure. With this paper, we introduce RoboRAN, multi-domain navigation framework focused robot-task decoupling for simplifying the development and evaluation of RL-based policies across different robots and operational mediums. Our framework enhances Isaac Lab (Mittal et al., 2023), providing unified tasks and robots definition, and allowing to train one policy per robot-task pair using& shared and extensible training infrastructure. While supporting all the robots shown in Figure 1, RoboRAN offers the following four contributions:
1. Modular design enabling task-robot interchangeaability. Our framework separates robot and task definitions using standardized APTs, minimizing integration overhead and enabling new robot-task pair- ings without altering existing modules. We demonstrate this interchangeability by training multiple navigation tasks across robots with distinct mobility systems (e.g., thrusters, wheels, water- propul- sion).
http:://gthiul.cm//tttprrrrr///
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Table I: Comparison of RoboRAN with existing RL frameworks.
Beschamurk
Tolk Typea
Sim--o-Reaal
Robrustaco
Roallsw
Modidarity
Budacud
RoboitAN (Chars)
Land/ / Water/ / Urbital
Navigation (6+)
(3 rohota)
Partial
Moderaate (realbetic plysice)
lsasc lab
BL-Nev X2 i al330 (iu ol, 3065)
Groud
Ner kes (1)
31 rhbu))
Partil
Madeeee trelitik plysin))
Pestill
Gaedho
Halens (Ber di 2011
luker
Resraggmmn Masiprhalice
Liuited
High jphoteesaeen seticalation)
Pectial
Brist
Bilvut Cyroraeimm (Gu
2001)
pooted todks)
Cooend Solo Itt, Mirriagenn
Vidturppionn
Melltrn
FiattBeacs (Na e: cL. 2023
Acriel (queizctie))
Egtrbiks aarigatlcn
di subu))
Parti
High. (cecankes, unkaa Miz)
Cunten BOs Gen
HAts (twitt et sl. 2011)
Grorn
Bearthee / Safe nair gitionn
Misttur / Love
Poetial Paciial
(Giaau 34 (Xa is al. 2015)
lukice
Issorariive assigatins Peene dorpoist surigetiis
Lasited
High (rvaiaiic semsors)
Giluus i PyBilks UndayD
Apuertc Jenttmars Wome et si., MtA
Water (sqputie]
Pertii
Madrmns (estrodonmmiis, den))
2. Sim-to-real transfer across diverse platforms with a single training and deployment pipeline. In contrast to previous work that primarily validates policies on a single robot type, we conduct real-world experiments on three distinct platforms: a Floating Platform, a Boat, and a Turtlebot2. We demonstrate how our simulation platform, with domain randomization and physically grounded disturbances, enables sim-to-real transfer, and report the corresponding performance results across robot-task pairs.
3. Open-source deployment API for Isaac Lab-trained policies. We release the first deployment stack that enables policies trained with skrl or rl_games in Isaac Lab to run directly on real robots. The stack removes dependencies on the Gym wrapper, supports lightweight execution on edge devices, and provides ROS 2 integration and Dockerized workflows for reproducibility, lowering the barrier from training to real-world validation.
4. Unified tasks and metrics for cross- moss-mediimm evaluation. We provide a common testbed for evaluat- ing navigation strategies in diverse set of operational conditions (e.g., terrestrial, aquatic, or microgravity environments). This is enabled by a reusable and extensible performance metric layor, applicable across navigation tasks and robot configurations.
2 Related Work
Reinforcement Learning (RL) has emerged as a powerful paradigm for control lasks, demonstrating its ability to learn complex policies directly from sensorimotor data. This has led to significant advancements across various domains, including robotic manipulation (Levine et al., 2015), humanoid locomotion (Peng et al., 2018), and the control of legged robots (Lee et al., 2020). While benchmark development has primarily centered on manipulation (Lee et al., 2019; James et al., 2019; Zhu et al., 2020; Heo et al., 2023), navigation remains fundamental aspect of embodied intelligence that has gained inereasing attention (Zhu & Zhang, 2021; El-Hariry et al., 2023).
To facilitate learning-basse navigation, numerous simulation environments and physics engines have been developed. Framoworks such as MuJoCo (Todorov et al., 2012), PyBullet (Coumans & Bai, 2016), We- bots (Michel, 2004); and Isaac Gym (Makoviychuk et al., 2021) provide efficient and scalable platforms for RL training, but are often constrained to single-domain settings or specific robot morphologies. Isaac Lab (Mittal et al., 2023) extends Isaac Gym by supporting diverse robotic platforms, though it lacks both structured evaluation suite for benchmarking RL policies across tasks and domains and the flexibility for interchangeable training of multiple tasks across multiple robots. Several recent. benchmarks have addressed learning- based navigation under specific environmental and sensory constraints. Habitat (Savva et al., 2019; Szot et al., 2021) targets high-level planning and mobile manipulation in photorealistic indoor environments. The BARN challenge (Perille et al., 2020) focuses on low-level control in cluttered scenes, while Flight- Bench (Yu et al., 2024) benchmarks ego-vision-basoo navigation for agile quadrotors. Aquatic navigation tasks are considered in (Corsi al., 2024), and iGibson 0.5 (Xia et al., 2019) provides an interactive bench- mark in household environments. These efforts, however, are typically domain-specific and lack support for robot-task interchangeability or sim-to-real evaluation.
Robustness and generalization have become important in recent benchmark development. RRLS (Zouitine et al., 2024) introduces worst-case robust control evaluation using adversarial domains in MuJoCo, while Robust Gymnasium (Gu et al., 2024) defines modular disruption models across 60+ tasks. Although these environments are well-suited to studying resilience in policy learning, they remain simulation bound and are limited in the diversity of robotic embodiments.
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Prior work such (Xu et al., 2023) identifies four key desiderata for RL in robotics (uncertainty handling, safety guarantees, data efficiency, and generalization) and provides valuable evaluation metrics and insights. The Gazebo- based simulation environment used in this work supports algorithm comparisons, but is limited to a single navigation task and robot. contrast, RoboRAN emphasizes multi-robot, multi-domain ficxibility within a high-throughput, GPU-acoelerated simulation stack. Its modular design and extensibility enable future integration of safety-focused features such as policy and environment constraints.
While robustness and safety-centric studies like RRLS (Zouitine et al.; 2024), Robust Gymnasium (Gu et al., 2024), and (Xu et al., 2023) focus domain shifts guarantees, RoboRAN provides complementary value by supporting simple real-world deployment and modular task-robot definitions, allowing practitioners to easily integrate different robot morphologies and new navigation tasks across diverse physical environments. Table compares RoboRAN with existing RL benchmarks along axes such as domain diversity, task types, sim-to-real validation, robustness testing, realism, and modularity. We highlight our benchmark's cross- domain reach, support for real-world deployment, and modular structure enabling extensibility.
3 RoboRAN Overview
Extens ible Set of Robots
Flosring
Kingtishi
Common Environment Manager
Randomization
Custom Physics
Train
Deploy
Water
Land
Pulicy
ROS2 API
Kingfisare
Tisk API
Evalusat
Performance Metrks
Set of Tasks
Rl. Model
Extensible
Track Veloorie
Go Pestions
Library
Turlebo:
Fleating Plarform
to Pakilin
Direct Environment
RoboRAN
Isaac Lab
Real Robots
Figure 2: RoboRAN framework: the navigation tasks and Simulation Robots modules, along with selected RL library of preference, serve as only inputs needed for our Environment Manager to train policy in simulation, providing readily available network for deployment on the real analog of the chosen robot.
RoboRAN is designed to train and evaluate robolic navigation lasks across a variely of operational settings. We introduce a unified structure where diverse robots can be evaluated on a shared set tasks, using consistent interfaces and metries. This design uniquely enables scamless interchangeability between agents and environments across different physical domains.
Our environment, formulated as standard Markov Decision Process (MDP) (Puterman, 2014), is defined by the tuple A, where S is the set of states, A is the set of actions, P(s' s, a,a) is the transition probability function, r(s, a) is the scalar reward function, and step 1, an agent observes a state 8, E S, selects an action 0t E A according to its policy <(a 8t), receives a reward rt = r(st, at), and transitions to a new state 8t41 ~ P(- 8t, at). The environment thus provides at each step an observation o E O, a reward rt, and a done signal d, E 10, indicating termination. The goal of the agent is maximize the expected return (TT E, 7 over episodes of length T.
[0, 1] is the discount factor. At each time
Figure 2 depicts the main components of our framework:
The Common Environment Manager instantiates a specified task-robot pair, dynamically configuring the simulation assets, physics paramcters, and task constraints based on the robot's specific characteristics and operational medium. This modular design is a key contribution of our framework, interchangeability between tasks and robots, and sub-module addresses distinct aspect of this flexibility.
enables full
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Table 2: Comparison of Robot Properties in RL Navigation Tasks. Control inputs are expressed as mathe- matical spaces.
Robot
Actuation Type
Degrees of Freedom
Control Input Space
Motion Constraints
Floating Platform
Thruster-based (binary)
3 [x, y. yaw)
{0, 1]s
No rolling/pitchiinn planar motion
Kingfisher 'Turtlebo22
Water-based thrusters
3 [x. y, yaw)
R² (left /tigtt thrust)
Drag and inertia effects, smooth but slow No lateral movement, limited turn spred
Differential drive
8 (x, y. yaw)
R² (e, w)
More details provided in Appendix A.7.
The Custom Physics module computes custom dynamics and actuation forces through parameterized thruster/proppp propeller models. For instance, it applies hydrodynamic and propeller models for surface vessels, microgravity and frictionless dynamics for the floating platform. It also enables flexible rewards to platform- specific constraints, such as penalizing rapid thruster actuation.
The Custom Domain Randomization module implements the disturbances detailed in Section 3.4 which are required to achieve sim-to-real transferability.
During training or evaluation, the Performance Metrics layer attach task-specific logging hooks enabling both on-line navigation metric updates and uniform post-hoe evaluation.
The ROS2 API simplifies policy deployment by using a ready-to-use inference node that exposes stan- dard ROS2 interfaces, eliminating manual policy export steps that differ across RL libraries. More in the Deployment Section 3.6.
Together, these sub-modules enables flexibility and streamline the pipeline that shortens the loop between Training, Deployment and Simulation for diverse types of robots. The relationship between RoboRAN and Isaac Lab detailed in Section 3.7.
3.1 Robots
RoboRAN supports all robots presented in Figure 1. Among them, we selected three representative robots for further evaluation and field tests. Their characteristics and control properties are summarized in Table 2.
Land. We selected the Turtlebot2, open source platform with differential drive system with non- holonomic dynamics. To demonstrate the extensibility of our stack, two more wheeled robots (Teatherback and JetBot) are supported and tested in simulation. Results and details are provided in the Appendix A.1. Water. We use the Kingfisher M200², a surface vessel with high inertial properties featuring a calamaran hull configuration and is driven by two fixed propellers, one on each hull. To simulate aquatic dynamics, we override Isaac Lab's default planar physics with custom hydrodynamics and hydrostatics models, enabling more accurate motion behaviors influenced by water resistance.
Space. We implement. floating platform, thruster-actualed system constrained to planar movement, mimicking opacecraft-like motion with force-based control. This robot, through air bearings mounted on its basc, generates a microgravity effect by pushing constant airflow against the floor to lift and levitate in a free-floatig fashion. To simulate this effect, we implement a custom frictionless dynamics to approximate free-floating orbital behavior, which is not natively supported by Isaac Lab.
3.2 Tasks
While Isaac Lab designs tasks around fixed robot models, RoboRAN decouples robot and task definitions, allowing consistent training and evaluation pipelines for any supported robot across all tasks. Our framework includes a suite of four navigation lasks designed to evaluate robotic motion in different environments and actuation methods. Each task leverages a structured observation space, detailed in Table 3, providing essential state information such as base velocities [V,, W|, which capture the lincar and angular velocity of the agent. To enhance temporal reasoning, 01-1; enabling the policy to infer dynamic transitions and improve stability in control. In all tasks, the observations are provided in the robot's 's own frame and apply Domain Randomization that mimics the noise of real sensors commonly used for state estimation.
augment the observation vector with the previous action
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Table 3: Summary of Navigation Tasks, Objectives, and Observation Space
Task
Objective
Obs
Obs Components
Obs Variables
Dim
GoToPosition
Reach target position
6
Base Velocities, Target Info
Iv Mys [d, sin())
[Va, "y 4/, [d, (ins
GoToeose
Reach a target 3DoF pose
8
Base Velocities, Target Info, Target Heading
[cos(e))ssin(e)) sin(e))
[Va, "y W/. [d, siss)))sin(()))
GoTThruugh Positions
Posiigonnssssssonnn
Follow a sequence waypoints
6 + 3m
Base Velocities, Target
Info, Future Goals
[ds, cos((:)) sin(O:)
TracoVelosities
Maintain set velocity
6
Error Terms, State
ICus Ety Puls Wy.
GoToPosition task requires the agent to reach randomly initialized 2D position using the target infor- malion (d, sOn,,,,,)))) representing the Euclidean distance and bearing to the goal. The relative angular and sin of the angle to ensure the observations are continuous (Zhou
position of the goal, is provided et al., 2018).
GoToPose task is similar to GoPoosiioo but also requires orientation alignment. Therefore, the obser- vation space incorporates the target heading as (v))()), sin(w)) to provide the angular distance to the desired final orientation.
GoThroughPositions task involves sequential navigation through a series of n waypoints, introducing fu- ture goals [d,, cos((,)),,)))))))))))).) sin(0,)] in the observation space to ensure smooth trajectory planning.
TrackVelocities task requires the agent to follow timo-varying velocity reference in both lincar and an- gular components. The observation space includes velocily error terms (en,,t, ét, ew, capturing deviations from the desired forward, lateral, and angular velocities. While no explicit path planner is embedded in the con- trol policy, the velocity references can be derived from any arbitrary trajectory generator, including spline interpolators or MPC-based local planners. In this sense, the generator acts as lightweight path planner, and the learned policy serves as a robust low-level controller that tracks planned motion commands across diverse robot morphologies and terrains.
These tasks provide flexible evaluation suite for RL-based navigation, adaptable to use-cases such autonomous docking, inspection, formation control, and trajectory tracking. While the core experiments in this paper focus on fundamental control-oriented tasks without obstacles or perceptual inputs, the framework is designed to support more complex scenarios. Thanks to its modular architccture, features such as obstacle avoidance, moving targets, and real-world sensing modalities can be integrated with minimal code changes. We provide illustrative examples of these extended capabilities, such as navigation with static obstacles, Appendix A.2, demonstrating the framework's 's applicability beyond the tasks reported in the main evaluation.
3.3 Reward Formulation
The reward function combines task-specific objectives with general regularization terms to ensure consistent goal-directed behavior and control smoothness across robot types. Its unified form is shown in Eq. where dy da, and de denote the distance to the goal position, heading misalignment, and boundary proximity respectively. The terms vj represent linear and angular velocities clipped to task-defined ranges, Ad, is the signed progress along the goal direction, and Iggaal provides terminal bonns when the goal is reached. The term robot adds optional robot-specific shaping such as control regularization.
Tt
Σ wse dilhe (E
Σ jE(w,))
Wi clip(Uj, Umin, Umax) + Wpg Adl + Whns Ibonus + r
robot
(1)
The weights Wi, Wi, Wpg: and Wsuce vary by task, and are denoted in Table 10 as as for GoToPosition, & for ToPse, &; for GoThroughhPositiine tasks. For example, ore dd//Ai encourages position convergence GoToPosition, while 3he - dp/>i da//a (dn//d jointly rewards alignment in ToPoo. Similarly, progress is captured by &i Ad, in Go CeroughPositionns
and 7 for Track Velocities with decay constants X; shared across

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and Track Velocities uses yH na/Do and stability across robots, and are reported in Table 10 of the Appendix A.4 for full reproducibility.
/ to penalize velocity tracking errors. All coefficients were tuned for balance
3.4 Domain Randomization
To support sim-to-real transfer, apply domain randomization in three key areas: (i) robot mass properties (mass, center of mass location, inertia tensor), (ii) actuation noise via Gaussian perturbations to commanded actions, and (iii) external disturbances modeled random wrenches applied to the robot's base. The amount of randomization is chosen at random at every reset. We ensure reproducibility through a per- environment seod-controlled random number generation (RNG) using Warp (Macklin, 2022), allowing fine- grained domain randomization across parallel training environments. We apply moderate randomizations to simulate real-world uncertainties. For the Turtlebot2, we vary its mass by #0.1 kg and CoM by =0.05 (std 0.01), reflecting typical manufacturing variances. For the Kingfisher, which operates in fluid environment, use broader mass (#2.0 kg) and CoM (+0.05 perturbations, and apply random body wrenches (forces E (.,0.25 N, torques E [0, 0.05) Nm) to account for water currents. For the Floating Platform, we use intermediate mass (+0. kg) and similar CoM and wrench ranges to model small-scale system variations and external disturbances. An extensive description of the domain randomization is provided in the Appendix A.3.
3.5 Training
We train RL algorithms using the skrl Cerrano-Muüoz et al., 2023) library, with PPO (Schulman et al., 2017) as the training algorithm. PPO was selected due to its stability in high-dimensional continuous control and its widespread use in RL robotics settings. Rather than comparing algorithms, our focus on demonstrating the decoupling of robot-task development within a unified framework. All experiments were run on a single NVIDIA RTX 4090. PPO was trained with default hyperparameters, listed in the Appendix A.5, and each robot-task pair converged in ~ 15 minutes average. The final sct of policies trained and used for evaluation are 12 (3 robots and tasks).
3.6 Deployment
A key contribution of this work is the open-sourcing of the first deployment stack that seamlessly integrates policies trained with skrl or rl_games into the Isaac Lab simulation and real-robot environments. Our deployment framework is composed of three main components: (i) state creation node, which constructs the observation vector required by the trained policy for given robot-task configuration; (ii) model inference node, which loads the exported policy checkpoint and publishes control commands; and (iii) a goal generation node, enabling dynamic task definition during execution.
To facilitate reproducibility and portability, the system is fully containerized with Docker, and offers direct ROS 2 integration for hardware deployment. Additionally, simulated OptiTrack module allows rapid evaluation of inference pipelines without requiring physical experiments. Importantly, we deploy the system without relying on the Gym wrapper, which enables light execution on edge devices without the need for workstation-gratt Isaac Lab can be deployed to heterogeneous robotic platforms with minimal adaptation, making the stack suitable for both simulation benchmarking and field testing.
resources to load the networks. This design ensures that controllers trained within
By open-sourcing this stack, we provide the community with the first unified and lightweight pathway from Isaac Lab training to real-world robotic control, lowering the barrier to reproducible RL deployment across platforms. Link to the code.
3.7 Relation to Isaac Lab
While Isaac Lab provides a flexible starting point for robot simulation and training, RoboRAN extends it into a modular, benchmark-ready framework tailored for reinforcement learning. Our stack introduces (i) a

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Table 4: Task Success Criteria and Thresholds. Each task defines success based on reaching position, orientation, velocity, or time-based constraints.
Task
Success Condition
Threshold
GoToPosition
Final position error $ Ep
Cp 0.1m
GoToe
'p and orientation error S Waypoints reached within Ctp
" 0.1m, 68 100
GoThroughPositionn
Ctp 0.2m
Track Velocilies
Maintain cv,
0.2m/ 88, 6w = 10
decoupled robot- task interface enabling easy re-use of tasks across robots and vice versa, (ii) standardized reward APIs and evaluation metries for consistent comparisons, (iii) Dockerized ROS2 deployment interface for seamless sim-to-real transfer, and (iv) a suite of field-validated scenarios on heterogeneous platforms (ground, water, microgravity). These additions transform Isaac Lab from a development toolkit into a ready-to-use research benchmark for RL in mobile robotics.
4 Simulation Results
We evaluate RL-trained policies in simulation across representative robot task pairs, reporting results for three multi-domain robots: Floating Platform, Kingfisher, and Turtlebot2. These cover diverse range of actuation models and navigation challenges, ensuring a broad evaluation scope.
4.1 Experimental Setup
Each policy trained for 3200 epochs using PPO, over random seeds per robot-task pair. During evaluation, we use GPU-accelerated Isaac Lab rollouts with parallel environments collect performance data from 4096 evaluation episodes per run. All results are reported as mean std across environments, We define task-specific success percentage of trajectorics that satisfy the task specific metries. Each metric is associated to a set of thresholds (ep; 60; Exp: cn, and ew. that are listed in Table 4): GoToPosition: distance to goal < Ep within fixed time budget.
GoToPose: both distance < ep and heading error < must be satisfied.
GoThroughPPsitiions count of waypoints reached in sequence within etp tolerance before timcout.
TrackVelocities: mean absolute tracking error for linear and angular velocity must stay below e, and em-
In addition to success rate (defined the percentage of episodes that meet task-specific thresholds), we report continuous evaluation metrics to capture control precision and stability:
Final Distance Error (m): Euclidean distance to the goal at the end of the episode. Heading Error (9): Absolute orientation difference at the final timestep [GoToPose only).
Time to Target (s): Duration required to reach the target precision threshold. Lower values reflect faster convergence.
Velocity Tracking Error (m/s): Mean absolute error between target and actual linear/angular velocities (TrackVelocities only).
Control Signal Variation (unitless): Standard deviation of control signals over the episode, reflecting smoothness or abruptness of control.
Goals Reached: Total number of intermediate targets successfully reached during sequential waypoint tasks (GoTTrooghhPositionn))
All these metrics aggregated in Table 6, enabling multi-dimeenionall comparison across tasks and robots.

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Training Performance Comperison: GoPosiiiio Task
Training Performance Comparison: GoToPase Task
Mean Reword
50
Robots
Robots
Rostingplatomm kingtsher Larthebo22
teebb22
foetingllatorm
cirglishee
-200
20
60
80
100
20
66
9
200
Training Steps
Training Steps
Training Performance Comparison: GoThroughPositions Task
Training Performance Comparison: TrackVelocities
280
300
Mean Reward
Robots turtebor22
Robots
singtsher
Instingplatoom kingfsher
80
goutingpuatorm turtlesot2
68
W
80
136
Teining Steps
Treining Steps
Figure 3: Learning curves showing rewards (mean + std) over sceds per robot, compared based on task.
Table 5: Wall-clock time per robot-task pair (mean + std over 5 seeds) in minutes [m].
Task
Floating Platform
Kingfisher
Turtlebot2
GoToPosition
7.35 0.02m
13.91 0.19m
11.55 - 0.14m
GoToPose
5.46 0.00m
11.53 = 0.16m
TrackVelocitie
5.08 0.18m
13.47 0.07m
11.28 + 0.02m
GoT'hrooghhPsstiionn
5.51 0.13m
13.47 0.35m
11.20 = 0.03m
4.2 Training Efficiency and Learning Trends
Figure 3 shows the training reward across 5 seeds, highlighting learning speed and convergence per robot. The FloatingPlatform achieves the highest asymptotic rewards, benefiting from direct actuation despite its discrete thrust model. Turtlebot2 converges reliably with moderate final returns, aided by low-dimensional control. Kingfisher shows slower and less stable lcarning, likely due its hydrodynamic complexity and inertia. Table 5 reports average wall-clock time per training run. The Kingfisher requires the longest training time, consistent with its complex dynamics. Turtlebot2 trains fastest among wheeled platforms. The unexpectedly short time for the FlontingPlatform suggests beneficial interaction between its discrete control structure and Isaac Lab's GPU-based parallelization. These differences motivate further study into simulation efficiency under varying robot dynamics.
4.3 Task Success and Performance Analysis
To complement the reward learning curves shown in Figure 3, we conduct detailed quantitative evaluation across all robot-task pairs. This evaluation uses standardized success metrics and control efficiency indicators (Table 6) collected over 4096 parallel trajectories per setting.
Figure presents the convergence curves for each robot-task pair. Shared tasks (Go ToPoiiin,,, Go Through- Positions, and Track Velocities) are plotted together for comparison, while specialized tasks (oo are shown separately.

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Orheaa Dilinne Coneegaass
(a) Cosoionitoo:: all robots.
(b) GoToPose (distance): Floating- Platform, Turtlebot2.
(c) CoToPase (heading): F Floating
Platform, Turtlebot2.
settroughottt somberin of Livieessee
indiviactter imnttrtion rerfrmmnce
(d) GoTThrouglossitions achieved (all robots).
goals (e) GoThroughPosition goals dis-
(I) TrackVelocities: linear (black) and angular (orange) velocity errors (all robots).
tribution (4096 evaluation envs, all robols).
Figure 4: Simulation results across robots and tasks. Performance comparisons for GoToPose, GolThrouuhhPositionn Platform and Turtlebot2 on GoToPose (distance, heading). (d) Number of goals achieved in GoThroughPo- sitions (all robots). (e) Goals distribution over 4096 parallel evaluation environments (all robots). (l) Linear velocity error in TrackVelocities (all robots).
and Track Velocities tasks. (a) All robots for GoToPosition. (b, c) Floating-
Table 6: Simulation evaluation metrics per task and robot (mean + std across 4096 envs, PPO- skrl). Metrics include: success rate (%), final distance error (m), heading error (9), time to target (s), velocity tracking error (m/s), control signal variation (unitless), and number of goals reached. "_" indicates non-applicable metrics.
Taok
Robot
Suremo Rate
Dist Err
Honding Eer
Time to Target
Lin Val Ber
Ang Val Err
Ctrl Var
Gunds Ramchad
GoToPaction
PlottingPaaffor
0.91 0.64
0.05 + 0.01
67.06 + 5.38
0.52 + 0.0t
Kingfisher
1.06 0.73
176.11
RR0.66
0.75 0.45
Turlebot2
0.01
0.07
0.26
PkottingPlatorr
0.01
0.05
Kingfisher
0.00
0.20
7:00 8.08
120.50
0.29
Torlsbut2
0.04
0.34 0.01
4.30 1.50
2.18
0/43 0.35
PlaetingPaaffrmm
1.00 0.00
2.35 + 0.25
65.18 1.03
0.32 0.00
13.57 0:33
Kinafisher
1.00 0,00
2.41 4: 0.79
53.29
18.66
0.43 4: 0.244
10.70 a: 2.84
Turlsbut2
1.00
0.00
1.79 + 0.06
101.50 + 12:25
0.13 :: 0.06
11.01 # 0.12
TrackVdoccties
PlottinPPactoraa
0.53 0..8
0.05 0.07
0:00 4: 0.01
0.45 d: 0.04
Kingisber
0.68 0.68
0.03 0.00
0.24 4: 0.02
0.62 d: 0.37
Torthebat2
0.00
0.01
0.11 0:001
0.15 0.00
GoToPosition and GoToPose The Turtlebol2 achieves the highest success rate in GoToPosilion with 0.99 + 0.01, benefiting from its differential-drisg system and precise low-speed control. The FloatingPlat- form follows with 0.94 + 0.04, while the Kingfisher lags 0.59 + 0.29 due to inertia and limited turning agility. These trends are confirmed in Figure 4a, where Turtlebot2 reaches the goal region fastest, followed by FloatingPlattorm and Kingfisher. In the Go ToPose task, both FloatingPlatform and Turtlehot2 succeed in reaching the target, with success rates of 0.99 + 0.01 and 0.84 + 0.04, respectively. Kingfisher is not evaluated due to its lack of heading control. FloatingPlatform achieves superior orientation control, with a heading error of 0.78 + 0.01%, compared to Turtlebot2's s 4.39º + 1.56", as shown in Figure 4c. Distance convergence also faster and more precisc for FloatingPlatform (0.02 0.01 m vs 0.14 + 0.01 m, Fig. 4b).
All three robots successfully complete partial trajectories (100% success rate), but differ in the number of goals reached. FloatingPlatform achieves the highest average at 13.57 + 0.33,
GoThroughPositions
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while Turtlebot2 and Kingfisher reach 11.01 + 0.12 and 10.70 = 2.84 respectively. These differences are reflected in Figure 4d (cumulative goals) and Figure 4e (distribution), where FloatingPlatform's performance is both higher and more consistent. Turtlebot2 shows smoother trajectories but fails to reach all waypoints within the time constraints, while Kingfisher's performance is more variable due to inertia limiting sharp turns.
TrackVelocities Floating/Platorrm demonstrates moderate success in tracking target velocities. Its linear velocity error 0.05 + 0.07, and angular velocity error is 0.03 + 0.01, better than both Turtlebot2 (0.02 + 0.01, 0.11 + 0.01) and Kingfisher (0.03 = 0.00, 0.24 + 0.02), as detailed in Table 6 and shown in Figure 4f. The high angular error for Kingfisher highlights the difficulty of fast heading corrections in water due to drag and momentum.
Success Rate Summary Table 6 confirms these observations across tasks. Turtlebot2 dominates in GoToPosition, FloatingPlatform leads in foothroughPosiitinns, and both Turtlebot2 and Floating(Plafform perform comparably in ToPoee In Track Velocities, all robots achieve reasonable success, but Kingfisher exhibits the highest angular tracking errors, limiting its overall precision. These trends visible in Figure supporting our conclusion that control effectiveness varies not only across robots but also across tasks.
4.4 Discussions
While RL. policies achieve high success rates, several robot-specific failure cases were observed. The Float- ingPlatform experiences oscillations near target positions due to force-based control lag. The Kingfisher struggles with understocring in tight waypoint sequences, making sharp turns difficult. The Turtlebot2, de- spite overall fast learning, exhibits difficulty in precise in-place rotations, leading longer lurning maneuvers in the ToPse task. These challenges highlight the need for refined reward shaping and constraint defi- nitions to improve task execution. Overall, the successful training of diverse robots on shared tasks, despite their differing actuation and mobility constraints, demonstrates the viability of unified cross-medium pipeline. The Turtlebot2's 's rapid convergence, the FlontingPlatformi discrete thrust limitations, and the Kingfisher's inertia-driven control difficulties highlight the importance of evaluating RL policies across het- erogeneous platforms.
5
Sim-To-Re fo-Real Results
5.1 Overview and Setup
We tested the trained policies on platforms (FlontingPlatform, Kingfisher, and Turtlebot2) across different navigation tasks. Real-world tests did not include the GoToPose task for the Kingfisher because wind, currents, and its non-holonomic motion often made goal poses unreachable. Track Velocities was not reported for the Turtlebot as it directly executes velocity commands, making performance evaluation trivial. Each robot-task pair evaluated over 4 to 10 independent trials, with key metrics summarized in 7.
Sim-to-real performance drops are shown in Table We omit results for the Go Postitonsssitinnss and Track Velocities due inconsistencies between simulation and real-world conditions. Specifically some task parameters (c.g. goal distances and target velocities) were adjusted to fit the physical testing arcas, making direct comparison with simulation results unfair. A full breakdown of real-world evaluation runs, including per-trial plots and setup configurations, is available in Appendix A.6 and the public repository.
5.2 Field Results and Task Trends
GoToPosition: All three robots successfully minimized the distance-to-goal across runs, with Turtlebot2 and FloatingPlatform reaching perfect success rates of 1.00, and Kingfisher at 0.667. Despite Kingfisher's slower convergence and higher final distance error (0.464 + 0.660), Turtlebot2 showed precise goal conver- gence (0.019 + 0.044), matching simulation trends. Figure 5c illustrates these results.
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FlostingPlatform. GoToPase Field Text Parformance
Turtiabat2 GoToPate Field Text Performance
4.-
EIDDCO err
250
43-
Food
14-
30
Diaracee free
Hostin Emror
23
10-
25
30
200
325
159
175
38
106
150
200
250
330
Timaslogs
Timoloss
(a) GolePPse:: FloatingPlatform
(b) GolToPose Turtlebot2.
Go ToPositionn: Comparison Across Robotts
Ge GoTTroughhostttons Field Teats Cumulattve Goats
and
25
2.0
15
65
30
JUM
201
200
85
500
150
1.0
195
(c) Co all robots.
(d) GoThrooggaPosition::: all robots.
Figure 5: Field test results for navigation tasks. Performance evaluation for GoToPose (FloatingPlat- form, 2)) Go CoToPosition (all robots), and Go MooTThoogPPsstiiee (all robots).
GoToPose: FloatingPlatform and Turtlebot2 both completed the task with high success rates (0.818 and 0.800, respectively), although heading aligmment remained challenge. FloatingPlatform achieved slightly better heading accuracy (3.68 + 2.37%) than Turtlebot2 (4.21 + 2.79"), as visualized in Fig. 5a b.
GoThroughhPosition::: Although evaluated in the field, this task's structure changed significantly from simulation due to physical constraints. As such, we only report cumulative goals without comparison sim results. FloatingPlatform consistently reached the most goals (6.00 + 0.06), followed by Kingfisher (5.33 + 1.03) and Turtlebot2 (4.60 0.55), as seen in Fig. 5d.
TrackVelocities: We report how accurately each robot tracks time-varying linear and angular velocity commands. FloatingPlatform achieved the lowest angular tracking error (0.041 + 0.019), benefiting from its symmetric thrust layout and rigid structure, while Kingfisher has a higher angular tracking error of 0.077 + 0.049), consistent with the challenge turning in water with drag-induced delay.
More results showing the repeatability the field experiments are reported in Appendix A.6.
5.3 Sim-to-Rest Comparison
To assess generalization, Table compares simulation and real-world performance on shared metrics for Go- ToPosition and GoPooo We observe modest drops in heading accuracy and final distance error, especially under noisy dynamics. However, success rates remain high, confirming strong policy transferability.
5.4 Discussions
Despite signals of good sim-to-real transfer, specific failure modes were observed. FloatingPlatform often overshot targets due inertia and delayed braking. Kingfisher drifted in tight turns, likely due to hydrody- namic lag. Turtlebot2 struggled to rotate in place precisely, especially during heading alignment.
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Table 7: Real-world task performance (mean + std). " indicates not applicable or not tested.
Task
Robot
Success
Final
Dist Err
Heading Err
Ang Vel Err
Lin Vel Err
Goals Reached
GoTuuPosition
Phutinnglattfrrm
1.00
0.004
0.009
Kingfisher
0.667
0.461
0.680
Turtlebot2
1.00
0.019 0.044
GoToPPse
FlostingPlacform
0.818
0.112 0.061
3.68 2.37
Turtlebot22
0.800
0.027 0.020
4.21 2.79
GoThrrughPoss
FloatingPlacform
1.00
0.112 : 0.061
6.00 0.06
Kingfisher
1.00
0.235 0.039
5.33 1.03
Turtlebot2
0.00
0.287 0.090
TrackVelocities
FloatingPlatform
0.175 + 0.027
0.056 # 0.009
Kingfisher
0.077 0.049
0.041 # 0.019
Turtlebot2
Table 8: Sim-to-real performance gap for shared metrics. Only GoToPosition and GoToPose are directly
comparable.
Task
Robot
Success (Sim +
Real)
Final Dist Err
A Heading Err
GoToPosition
FloatingPlatform
0.94 1.000
0.05 + 0.014
Kingfisher
0,59 0.667
4 1.06 0.460
Turtlebot2
0.99 + 1.000
J. 0.07 - 0.019
GoToPosc
FloatingPlatform
0.99 + 0.818
1 0.02 - 0.112
1 0.78
3.688 4.39
Turtlebot2
0.84 0.800
0.14 0.027
4.21
These observations suggest opportunities for improvement via adaptive feedback control, recurrent models, or further domain randomization, especially for tasks requiring fine heading convergence or dynamic stop conditions.
6 Conclusions
RoboRAN introduces a unified framework that decouples robot and task definitions, enabling reproducible training, evaluation and deployment four navigation tasks on three heterogeneous robots spanning land, water, and microgravity. All robot-task pairs train in simulation within minutes and are assessed with con- sistent success metrics, facilitating direct comparison of learning progress and control efficiency across media, Policies learned in simulation transfer to the physical Turtlebot2, Kingfisher, and Floating Platform with high success rates and centimeter-level position errors, confirming effective sim-to-real generalization, though heading alignment and inertia-related failures highlight that the sim-to-real gap can be further minimized. Future work will extend RoboRAN to more complex tasks and additional robots, and its modular design provides a practical foundation for advancing robust autonomous navigation and studies of generalization across dynamics and platforms. In particular, we identify RoboRAN as a clean foundation for integrating transfer learning, multitask training, or policy distillation approaches.
References
Luis FW Batista, Junghwan Ro, Antoine Richard, Pete Schroepfer, Seth Hutchinson, and Cedric Pradalicr. A deep reinforcement learning framework and methodology for reducing the sim-to-real gap in asv navigation. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1258 1264. IEEE, 2024.
Davide Corsi, Davide Camponogara, and Alessandro Farinelli. Aquatic navigation: A challenging benchmark for deep reinforcement. learning. ANiv:2405.20532 2024.
Erwin Coumans and Yunfei Bai. Pybullet, a python module for physics simulation for games, robotics and machine learning. Xiv, 2016.
13

---

Published in Transactions on Machine Learning Research (11/2025)
Matteo El-Hariry, Antoine Richard, and Miguel Olivares Mendez. Rans: Sighly-parallelisee simulator for
reinforcement learning based autonomous navigating spacecrafts. In 17th Symposium on Aduanced Space. Technologies in Robotics and Automation (ASTRA 123), 2023.
Matteo El-Hariry, Antoine Richard, Vivek Muralidharan, Matthieu Geist, and Miguel Olivares-Mendez. Drift: Deep reinforcement learning for intelligent floating platforms trajectories. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 14034-14041. IEEE, 2024.
Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazundar, Yuejie Chi, Adam Wierman, and Costas Spanos. Robust gymnasium: A unified modular benchmark for robust reinforcement learning. Github, 2024.
Minho Heo, Youngwoon Lee, Doohyun Lee, and Joseph J. Lim. Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation. XrX::2000 12821, 2023.
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J. Davison. Rlbench: The robot learning benchmark & learning environment. wrXiv:1900112211 2019.
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter. Learning quadrupedal locomotion over challenging terrain. Science Robotics 2020 Vol. 5, Issue 47, cabc5986, 2020. doi: 10.1126/
scirobotics.abee598..
Youngwoon Lee, Edward S. Hu, Zhengyu Yang, Alex Yin, and Joseph J. Lim. Ikea furniture assembly environment for long-horizon complex manipulation tasks. 19X1:1911.072// 2019.
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. End-to-end training of deep visuomotor policies. arXie: 5000.00002 2015. Available at arXiv:
Miles Macklin. Warp: A high performaeee python framework for gpu simulation and graphics. https: //gtthub. com/ nvidia warp, March 2022. NVIDIA GPU Technology Conference (GTC).
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al. Isaac gym: High performance gpu-basod physics simulation for robot learning. arXiv preprint ar Xiv:2110. 104 70, 2021.
Olivier Michel. Cyberbotics Itd. Wc professional mobile robot simulation. International Journal of Advanced Robolic Systems, 1(1):5, 2004.
Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, and Animesh Garg. Orbit: A unified simulation framework for interactive robot learning environments. 1EEE Robotics and Automation Letters, 8, June 2023. doi: 10.1109/LRA, D..1009/LRA.2023.327700 2023.3270034.
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. Deepmimic: Example-guided deep reinforcement learning of physics-based character skills. arXiv:1804.02711 2018. doi: 10.1145/3197517 3201311.
Daniel Perille, Abigail Truong, Xuesu Xiao, and Peter Stone. Benchmarking metric ground navigation. In 2020 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR). IEEE, 2020.
Martin L Puterman. Markov decision processes: discrete stochastic dynomic programming. John Wiley &
Sons, 2014.
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra. Habitat: Platform for Embodicd AI Research. In Proceedings of the IEEE/CVF International Conference Computer Vision, 2019.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov, Proximal policy optimiza- tion algorithms. arXiv: arXio:1707.068 1707006347, 2017.
14

---

Published in Transactions on Machine Learning Research (11/2025)
Antonio Serrano-Muñooe Dimitrios Chrysostomon, Simon Bøgh, and Nestor Arana-Arexolaleiba. skrl: Mod- ular and flexible library for reinforcement learning. Journal of Machine Learning Research, 24, 2023.
Hartmut Surmann, Christian Jestel, Robin Marchel, Franziska Musberg, Houssem Elhadj, and Mahbube Ardani. Deep reinforcement learning for real autonomous mobile robot navigation in indoor environments. arXiv:2005. 13857, 2020.
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, et al. Habitat 2.0: Training home assistants to rearrange their habitat. Advances in Newral Information Processing Systems (NeurIPS), 34:251-266, 2021.
Hamid Taheri, Seyed Rasoul Hosseini, and Mohammad Ali Nekoui. Deep reinforcement learning with en- hanced ppo for safe mobile robot navigation. (xXi:2205.162860 2024.
Mohammad Dehghani Tezerjani, Mohammad Khoshnazar, Mohammadhamed Tangestanizadeh, Arman Kiani, and Qing Yang. A survey on reinforcement learning applications in slam. arXiv:2408.1454 145188 2024.
Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pp. 5026- 5033. IEEE, 2012.
Mariusz Wisniewski, Paraskevas Chatzithanos, Weisi Guo, and Antonios Tsourdos. Benchmarking deep reinforcement learning for navigation in denied sensor environments. arXiv:2410. 10. 14616, 2024.
Fei Xia, William B. Shen, Chengshu Li, Priya Kasimbeg, Micael Tchapmi, Alexander Toshev, Li Fei-Fei, Roberto Martin-Martin, and Silvio Savarese. Interactive gibson benchmark (igibson 0.5): A benchmark for interactive navigation in cluttered environments. IEEE Robotics and Automation Letters, Vol. 5, No. 2. April 2020, 2019. doi: 10. www..R............2 2020.2965017.
Zifan Xu, Bo Liu, Xuesu Xiao, Anirudh Nair, and Peter Stone. Benchmarking reinforcement learning tech- niques for autonomous navigation. In 2023 IEEE International Conference Robolics and Aulomation (ICRA), pp. 9224-9200 9230, 2023. doi: 10. 10.1109/ 2CC..44899..023..11
Shu-Ang Yu, Chao Yu, Feng Gao, Yi Wu, and Yu Wang. Flightbench: Benchmarking learning-based mot for ego-vision- based quadrotors navigation. arXiv preprint arXiv:2406. 05687, 2024.
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. On the contimuity of rotation representations in neural networks, 2018.
Kai Zhu and Tao Zhang. Deep reinforcement learning based mobile robot navigation: A review. Tsinghua Science and Technology, 26(5):674-69)) 2021. doi: 10.26599/TST 000009999TTT.2221910000000 2021.901001...
Yuke Zhn, Josiah Wong, Ajay Mandlekar, Roberto Martin-Martin, Abhishek Joshi, Kevin Lin, Abhiram Maddukuri, Soroush Nasiriany, and Yifeng Zhu. robosuite: A modular simulation framework and bench- mark for robot learning. arXiv:2009. 12293, 2020.
Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, and Emmanuel Rachelson. RRLS: Robust Reinforcement Learning Suite. Xrv, 2024.
A Appendix
A.1 Wheeled robots
Along with the TurtleBot2, which we selected for real-world deployment and sim-to-real evaluation, Rob- oRAN also supports two additional wheeled robots in simulation: the Leatherback, NVIDIA's rescarch platform for autonomous driving, and the JetBot, an open-source educational robot widely used for hands- learning in robotics and AT.
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The TurtleBot2 and JetBot both rely differential drive system, enabling planar movement through independent control of left. and right wheel velocities. This shared control structure supports consistent evaluation across these platforms. In contrast, the Leatherback features an Ackermann steering mechanism, better suited for high-speed and realistic road-like navigation. It offers advanced capabilities, including larger actuation limits and support for real-time ouboard computation, making promising platform for future exploration of perceptually guided or high-speed navigation tasks. The JetBot, by comparison, lightweight and cost-effective, making ideal for rapid protobyping and student-level research.
While these two additional platforms were not included in physical experiments, they are fully integrated into the RoboRAN framework and can be readily used to train and evaluate navigation tasks in simulation. Figure 6 shows their simulation models rendered within Isaac Lab.
Figure 6: Left to right, first row Jetbot in GoToPosition, TrackVelocities, GoThroughPosstiins,, second row Leatherback GoThhouugPositioonn CoTTaPositooWWithhhsseooooo GoToPose
A.2 Extended Environments and Task Variations
Although the four tasks described in the main paper are designed to evaluate core aspects of navigation, Rob- oRAN supports more complex environments and objectives. Its modular task interface allows users to easily introduce obstacles, moving targets, or perception-driven goals without altering the robot implementation training pipeline.
To demonstrate flexibility under environmental constraints, evaluate the GoToPosition task with static obstacles placed between the robot and its sequence of goals. Using the same reward structure and PPO hyperparameters as in the base task, without task-specific tuning, all three robots (Floatingglattfomm, King- fisher, Turtlebot2) succossfully learn to navigate around obstacles (Fig. 8). Training curves (Fig. 7) show rapid improvement followed by stable convergence, with final mean rewards over the last 50 steps of 93.4 (Kingfisher), ~ 75.4 (Turtlebot2), and 73.0 (FloatingPlatfomm. The observation space extended from the base task by appending the positions of the three closest objects in addition to the original six dimensions.
Additional complex scenarios, such manipulation-inssil also supported but are left out of the main scope. These can be integrated with minimal code changes and will be shared in future iterations of the framework.
tasks (e-g., push-block for wheeled robots), are
A.3 Domain Randomization
We adopt a domain randomization framework as a standard tool to facilitate sim-to-rcal transfer, perturbing environment and robot parameters during training. The framework supports multiple application timings: reset-based (applied once at the start of an episode), step-based (applied every simulation step), action-
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Training Performance Comparison: GoToPosition Task (Obstacles)
Mean Reward
-100
-200
Rabots
-300
flostingplatferm
kinghtsher
400 Training Steps
800
Figure 7: Training performance GoToPositinn with static obstacles for three robots. Curves show mean and std reward over 10 seeds. All robots learn stable obstacle-avoiding behaviors; the final-50-step mean rewards are Kingfisher 933.4, Turtlebot2 = 75.4, and FloatingPlatforna 733..
Figure 8: FloatingPlatform, Kingfisher, and Turtlebot2 in ColoPesitoonWilhhsssaadssss
based (applied when processing control actions), and observation-basel (applied when processing sensory observations).
Table summarizes the available randomization types, supported modes, application timings, and descrip- tions. These include varialions of physical parameters such as mass, center of mass (CoM), and inertia, well as noise and scaling in actions and observations, and exogenous disturbances in the form of wrenches. Both stochastic (uniform (uniformnnorma)) nnomall and deterministic (decay, sinusoidal) patterns are supported, and modes can be combined.
Each randomization category is parameterized control its magnitude, distribution, affected elements, and temporal behavior. For example, MussRandomizationC) normal distribution, apply gradual decay either over time function of action magnitude. Similarly, CoMMRandomizationCCC offsets the center of mass in two or three dimensions, and InertiaRandomization Cfg perturbs the inertia tensor.
vary body's mass according to uniform
The framework also supports stochastic perturbations at the action and observation interfaces. NoisyAction- sCfg injects bounded uniform or Gaussian noise into control commands, while ActionsResealer Cfg applies multiplicative scaling to emulate actuator gain variability. NoissOObserattionsCC adds perturbations to sensed state variables, with optional normalization after noise injection.
External disturbances are modeled through WrenchRandomiiationCC. which can produce sporadic "kicks" or continuous wrenches, following uniform, Gaussian, or sinusoidal patterns. Parameters include affected body names, force and torque ranges, kick frequencies, and waveform characteristies.
The randomization system implemented on top of a core class (RandomizationCore)) that provides mode dispatch, timing control, environment integration, and logging facilities. Each specific randomization type inherits from this core, specialixing the parameter perturbations and application rules. Finally, the
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Table Randomization types, modes, application timings, and descriptions.
Type
Mode Name
Applied At
Description
Mass
uniform
Reset
Uniform mass variation at episode start Normul-distribtted mass variation at episode start
Mass
normal
Reset
Mass
constant_ decay
Step
Mass decays over time during an episode
Mass
oction_basede _ecca
Step
Mass decays proportionally to action magnilude
CoM
uniform
Reset
Uniform CoM offset at episode start
CuM
normal
Reset
Normul-distributed CoM offset episode start Spring-like CoM behavior (placclolder) Uniform inertia variation at episode start Normal-distributed inertia variation at episode start Inertia decays over time Unilorm noise sampled on reset, applied during actions Normal noise sampled on reset, applied during actions Uuiform scaling factors sampled on reset, applied during actions Uniform noise sampled on reset, applied during observations Normal noise sampled reset, applied during observations Sporadic auiforn-distrbbteed force/ pare/tt disturbances
CoM
spring
Step
Inertia
uniform
Reset
Inertia
normal
Reset
Inertia
decay
Step
Actions
uniform
Reset + Action
Actions
normal
Reset + Action
Roscaler
uniform
Reset
Action
Obscrvations
uniform
Reset Observation
Observations
normal
Reset Observation
Wrench
uniform
Reset Step
Wrench
kick_noree nornal
Reset + Step
Sporadic normal-distribated force/torqu disturbances
Wrench
constant_unifors
Reset + Step
Constant uniforrmdistriuuteed wrenches applied every step Constant normal-distributed wrenches applied every step
Wrench
ceaatant_nomaa
Reset + Step
Wrench
constant_ sinusoidal
Reset + Step
Simusoidally varying wrenches applied every step
framework remains compatible with perturbation utilities (e.g., rigid body material randomization, actuator gain variation, gravity changes) from Isaac Lab. Code 1 shows sample configuration class combining multiple randomization lypes. In this example, randomized uniformly at reset; action commands are both noised and rescaled; and sporadic force/torque kicks are applied to designated body.
is perturbed both at reset and via time decay; CoM is
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@configelase
class RobotCig:
rese_rand_cfg
Massianndonizationctg
enable-True,
conooniaationnodeeeentneee nody_name="core,, max m mass_change_rate==00.
"conatant_cime_deca
10
com_ com_rand_cfes cfg GoMBandomizationnCgg
11
enable=True,
landonization_mddes-t body_name="coree
13
max_delta=0.05
noisy_actions_ctt SoiayActionacfg
enable=True,
mandomizatton_modeeeee modes=="mmnforr")
elices-[(0, 2)1.
max_delta-[0.023]
21
clip_sctions- [(-1,
22
23
actions_rescalee:
ActionaRescalerCfg
24
enable-True,
25
26
slices=[(0, 2)),
27
rescaling_ranges- 8, 1.0)),
28
clip_actions= 1))
)
vrench_rand_cff Arenchhandorizattinncfg
enable=True,
randomization_motee (oodess""kickk. (nicc__
body_ core"",,,""
foiform_force-00, 0.25),
mniform_torque=te 0.05), intervlls
38
37
Code 1: Example mulli-randommzztion configuration.
A.4 Task specific reward parameters
Table 10: Reward parameters for PPO training. Task-specific coefficients and decay values used in Equa- tion
GoToPosition
GoToPose
GoThroughPos.
Traccielocities
0a (pos)
1.0
An (pose align)
1.0
Φn (progress)
1.0
"/s (lin vel err)
- 11.
042 (head)
0.25
Bi1 (lin vel)
0..5
01 (head)
0.05
"/2 (ang vel err)
- 0.5
0:11 (lin vel)
-0.05
Bi2 (ang vel)
00.5
Φ51 (lin vel)
0.0
7i (bonus)
0.0
0,2 (ang vel)
0.1
Bonst (boundary)
-10.0
02 (ang vel)
0.05
Tinst (boundary)
-10.0
Obnsl (bonus)
10.0
Apgi (progress)
0.2
Øbnsl (borus)
100.0
\1 = 1.0 (dist)
Ag = 0.25 (head)
A = 1.0 (bnd)
\ = 1.0 (vel err)
19

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Published in Transactions on Machine Learning Research (11/2025)
A.5 Algorithm hyperparameters
Table 11 summarizes the PPO hyperparamelerss used across all robot- pairs. These values are derived from our default training configuration used in all experiments.
Table 11: PPO hyperparameters used in all experiments.
Parameter
Value
Rollouts per update
32
Learning epochs per update
8
Mini-batches per epoch
8
Discount factor (7)
0.99
GAE parameter (A)
0.95
Learning rale
5 10
Learning rate scheduler
KT.AdaptiveI.R (threshold = 0.008)
State/valus /aluue preprocessors
RumiingStandardSSaler
Gradient norm clipping
1.0
Clipping ratio (e)
0.2
Value function clip
0.2
Clip predicted values
True
Value loss coefficient
2.0
Entropy loss coefficient
0.0
KT. threshold (early stop)
0.0
Time- limit bootstrap
False
A.6 Field tests repeated experiments
In this section we provide additional details from the field trials. Figure 9 shows multiple trajectories for different combinations robots and tasks, and Figure 10 provides additional metrics for the GoToPosition task across all three evaluated robots: FloatingPlatform, Kingfisher, and Turtlebot2. vations are affected by onboard sensor noise and world environmental factors. The results highlight the consistency and cross-platform transferability of the learned policies under realistic operating conditions.
these trials, obser-
The Turtlebot2 (Fig. 9a) exhibits consistent, nearly straight-line paths with minimal deviation across trials. The differential drive system enables precise low-speed control, resulting in rapid and stable goal convergence. The small variability between runs is attributed to minor noise in velocity exccution and sensor estimates.
In the case of the Kingfisher (Fig. 9b), trajectories show larger variance and slower convergence, particularly in the final approach phase. This behavior reflects the platform's 's higher inertia and complex draggddom dynamics, which lead to increased difficulty in performing precise manouvers, especially under subtle envi- ronmental disturbance, such wind or current. These results suggest opportunilies to reduce the sim-lo-real gap through improved dynamics modeling.
For the FloatingPlatform (Fig. 9c), trajectories remain tight and exhibit rapid convergence to the target position, with small lateral overshoots likely due to inertia and the discrete nature of thrust control. Despite minor oscillations near the goal, all runs demonstrate high repeatability and stable stopping behavior.
Across all platforms, the consistency of trajectory profiles mirrored by relatively low action variability during the task, confirming that policies rely smooth and robust control strategies even under unstruc- tured real-world conditions. These results reinforce the generalization of our learned policies in real-world deployments across different mobility systems, especially when operating under noisy sensing, actionion and environmental disturbances.
20

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Published in Transactions on Machine Learning Research (11/2025)
Task: Go Postongs
Task: GoToPose
Task: GoToPesition
Trajectory
Traectory
Tajastory
13
1.0
100
(a) Turtlebot Trajectories
Task: GoThroughPesitions
Task: GoToPosition
Task: TrackVeloitiee
Trjjecory
Traestory
Tra ectory
12
1
08
21
15
10.3
135
A.Pcalo1 170
APtabaan (m
(b) Kingfisher Trajectories
Task: GoToPose
Task: Go SoToPosition
Task: TrackVeloctties
Tajectory
05
0%
-05
-15
-20
-28
-15
-35
2.No
(c) Floating Platform Trajectories
Figure 9: Robot trajectories in real-world experiments. In all graphs, the colored dotted lines represent the robot trajectories, with different colors corresponding to different runs. For the tasks Go Throught Positions. ToPse, and Possitio,,,, the circles indicate the final targets. For Track Velocities, the black dots denote target positions generated by a trajectory generator.
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Published in Transactions on Machine Learning Research (11/2025)
FloatingPlattorm
Task: GoToPosition
Kingfisher Task: GoToPotttoo
Turtictot Tack: GoToPocition
Dislance Eru Over Time
Dislance Erve Over Time
Dilaance Eror Orei Time
O'aaanc Croy mm
D'stance Cio (m))
Paan 2000 1nd
Gool
Sool
Scal
Goal
Goal
Seal4
Badl
2nd
a00le
0001
boo
2003
Time 51
Time 1S
Time
Actions Deer Time
Azbons Over Time
Action Doet Time
Nctions (ba))
D2-
20
-0.5-
65-
00
1.0
Action Diffrrecces Over Time
Action Diffrrnces Over Time
Action Differences Over Time
2.0
08
wedon offfeeee (80)
Action
00
3.
30
Arrumiatted Artinn Craor
oatted ware
120
Cortrot Memertoo
25
20-
(a) Floating Platform
(b) Kingfisher
(c) Turtlebot2
Figure 10: Repeated field trials of the CoPoiiin task across three robotic platforms. (10a) Floating- Platform: The metries demonstrate consistent convergence, and the actions highlight the use of discrete thrust commands. The thrusters are engaged continuously to maintain position and prevent drift after reach the goal. (10b) Kingfisher: The metrics indicate that the policy often overshoots the goal, triggering cor- rective actions and suggesting potential improvements to the dynamics model to reduce the sim-to-real gap. (10c) Turtlebot2: The differential-drive system yields stable and repeatable trajectories with small final distance errors. In two experiments, it shows oscillating actions after reaching the target position, which indicates noisy position estimation.
A.7 Customization of Environments
The framework is built on a modular and extensible architecture that significantly streamlines the process of adding new robots or tasks. This design eliminates the need to create a new environment. from scratch for each addition. The core of this system is the use of factories within the environments directory, which dynamically generate the necessary components.
The file structure demonstrates this clear separation of concerns:
22

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Published in Transactions on Machine Learning Research (11/2025)
robots: Contains the code for different robot models. To add robot, you simply create new Python file (e.g., robol 2-y)) that inherits from robol_ core.py.
tasks: Houses the definitions for various tasks, Adding a new task involves creating new file (e.g.. task 2.py) that extends task_core.py
source
isaaclab_ task
direct Isaac Lab code managed Isaac Lab code
<-- Our Framework
domain_randomization environments
Single
single_env. <-- NO CHANGES NEEDED
10
Agents
11
skrl
12
rl.games
13
rsl_rl
14
robots
15
robot_core
FloatingPlattorm.
bev_robot_1.pp <-- add here
robots_cfg
20
tasks
21
task_core.pt py
GoToPosition.py
23
24
new_task_1 1aakk_.ppp py here
26
tasks_cfg
26
utils
Code 2: Folder structure of our framework integrated inside Isaac Lab.
This approach ensures that the fundamental enviromment code remains unchanged. By inheriting from a class and adding the new component (be it new task framework automatically integrates it. This not only reduces development time by needing to write less code but also minimizes the risk of introducing errors into the core environment.
robot) to its corresponding folder, the
Code 3 shows the structure of the environment. with the API for the robot and tasks.
class SingleEnr (DirectRLEv))
ofg: SingleEnvCfg
eam___ee_ppceeeeee__ (self):
"""Configure the action and observation spaces Gyn environment... "** observation space (unbounded since ve don't impose any limits) super hewrriuure_g__ee__peee self. self. Beatigure__y__e__epeee action sample_space (self... single aaale__cctio__ppcee_ self. sim. device, batch_sizer self num_envs, fill_value=0)
10
self.action_epe space self robot_api.
11
12
13
def Setuup_sceneBse (self):
14
self robot = (see wtw.aatoonnnss..oooooooooooooooo rwbot_ofg..oooooee robot_cfg
self robot_obapap.. ROBOT_FACTORY
self.cig.ze robot_name.
scene=self. scene.
cobot_cfgrsalf..rooottcoo robot_uid=0, aun_enva=self. num_emi envs,
decimation=self decimatii,,,
device=self device,
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Published in Transactions on Machine Learning Research (11/2025)
24
self. tae TASK_ FACTORY (
25
self efg. task name, scene-self.. scene, task_cfg=self tag task_uid=0, num_enve-self, nun_eave, device=self.des device,
26
27
28
29
30
31
32
38
self. reg.sterrrttttrrooooooo (self. rob.rreo.........otooo.
self task_api register_sensors
# add ground plane
ppaau_ground_plaas (ptttaatttattoo/oo/////////ree
efg-GroundPlaneCfe ())
# clone, filter, and replicate
self scene. envormmnnvissmmettt ccoy__tron_ourceeFaalaa.
40
self scene filter_collisions (global..rimm.pahhs_]] add articultion
41
42
self. scene. artiuuaitiis [selffcfg..........e..eeeelll reebt...g. self robot
43
lights
44
light_cfg sin sim_utils... Dew.Ligghcfggnnnennttt---- ., color=(0.75, 0.75, 0.75)
46
light_cig.f func ("Mo(o//rld/Lggh light_cfg)
48
47
def pwr_ppyeiic_steppee (self, actions: torch. Tensor) -> None:
48
self ralffrobot_aee api
caatiionnoaciinns.. (actions)
49
50
def appply_action >> None:
51
self robot apt..pp.ppplll.ctiioo
def _et__ osseervvatiionsss111 -> dict: task_obe obs self task_ spi get_ebbeevations ()
observations ("policy": task_obs) return observations
def rett_ torch. Tensor:
self taak_ compute se
61
def _et dete_ (self) [torch Tensor, torch, Tensor]: bobot__earl_ttrmtttte
82
terrinaattnnnnnionn self.robor roboot_app. htcp__app.gee_deeee gene O)
63
wew.cllanntemmaattiin self task_ap1 get_ ()
64
65
time_out self spisode__ength__eeee >= self max_ mox_opisoode Parly_terninate
66
task_ termination tasa_eerry_terminatie
07
clean_termination = cobot_cleaa_terrinnatt
time_out
return early_terminse wet.ttermnnniio... clean clean_terminatti
Code 3: Demonstration how the environment interacts with the robot and task components through the API. This dynamic instantiation ensures that the environment
hard-coded for a specific robot or task. This modularity not only reduces development time but also enhances maintainability and robustness by isolating component-specife code from the core simulation loop.
A.7.1 Algorithmic variation
In addition, thanks the workflow inherited from Isaac Lab, users can easily select different learning library algorithm by modifying the configuration files rather than the code itself. As illustrated in Code 2, the Agents directory organizes all configuration files where training settings specified. Swapping PPO for another algorithm (such as SAC or TRPO) simply involves changing a single entry in the corresponding YAML file, making the framework agnostic to the underlying learning implementation.
24


Reviews:
[{"review_id": "LKsWg80GJF", "reviewer": "Reviewer_hMQx", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes a new open-source framework for benchmarking robotic navigation acrosscombinations of environment modalities (land, water, space) and tasks (position, pose, waypoints, velocities). A single reward is proposed to unify different task types, such that any policy for a chosen robot-task can be trained using that reward. The framework supports domain randomization to enable training and deploying policies for sim-to-real transfer.", "claims_and_evidence_yn": "No", "claims_explanation": "1. The paper claims \"Our framework separates robot and task definitions using standardized APIs, enabling fast experimentation with new robot–task pairings and facilitating the extension to novel platforms and navigation tasks\". The fact that this paper demonstrated three different motion modalities (thrusters, wheels, water propulsion) is good but not sufficient to support the claim that extending to a new platform is \"fast\". As currently presented, the paper does not show how difficult it is to use the framework to add a new task or customize/modify/add some new motion modality or dynamics. This is just a writing issue with the paper: I am not doubting the possibility of such extension, but rather just pointing out that the claim is not supported. One would expect something along the lines of \"it took one hour of human effort to add a new task\", or \"here is the API, see how easy it is to customize\" (this can be in the appendix. The linked code is not final so reviewers are not expected to read it).\n2. Only PPO was run. Yes, comparing algorithms is not the point, but at least show that your framework allows training and deploying using various algorithms. Running some would be good (who knows, maybe there is high sensitivity to algorithms and need tuning, then your framework would have to support tuning). Otherwise, showing some snippets of the API in the appendix may be enough.", "audience_yn": "Yes", "audience_explanation": "There is wide interest in RL and robotics.", "requested_changes": "1. Address the above comment about the unsupported claim of ease of customization/adding new robots/tasks", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "K0tDf14ATn", "reviewer": "Reviewer_vMJe", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents RoboRAN, a unified and modular robotics framework designed to facilitate reinforcement learning (RL) across diverse robot platforms and tasks. Built on top of IsaacLab, RoboRAN enables interchangeable training of policies for multiple robot-task pairs within a shared and extensible infrastructure. The framework offers a consistent definition of robots and tasks, supporting reproducible experiments and scalable policy training. RoboRAN’s effectiveness is demonstrated through both simulation and real-world sim-to-real transfer experiments, involving a range of robotic platforms such as satellite simulators, unmanned surface vessels, and wheeled ground vehicles.\n\n## Strengths:\n- The multi-domain approach addressing terrestrial, aquatic, and space robots broadens applicability beyond typical RL frameworks.\n- Real-world experiments validate the sim-to-real transfer capability, strengthening the practical relevance.\n- Open-source release encourages reproducibility and community engagement.\n- Uniform evaluation protocols improve benchmarking fairness and comparisons.\n\n## Weaknesses\n- It remains unclear how seamless and scalable the integration process is for even new robots and tasks\n- The paper demonstrates training separate policies for each robot-task pair but does not justify the need for a unified framework if policies remain isolated. It lacks evidence that the framework enables knowledge sharing or transfer learning across different robots or tasks, which would better motivate its multi-domain approach and highlight novel contributions.\n- Could the authors please elaborate on how the claimed interchangeability is demonstrated through training multiple navigation tasks across robots with distinct mobility systems (e.g., thrusters, wheels, water-based propulsion)? Specifically, it would be helpful to clarify whether this interchangeability involves shared policies, transfer learning, or simply training separate policies within the same framework.", "claims_and_evidence_yn": "Yes", "claims_explanation": "I think the authors have empirically support all their arguments and claims and I do believe that they have a huge contribution in robot learning community from application perspective. However, I am not convinced by the connection between the motivation and the proposed framework. From a practical standpoint, if a lab operates only one robot type, a dedicated platform for that robot may suffice, reducing the need for a unified framework. Conversely, when multiple robot types are involved, it’s unclear what benefit arises from having all robots solve the same task independently without collaboration or knowledge sharing. Without enabling inter-robot communication or transfer learning, the practical advantages of this multi-robot platform appear limited.", "audience_yn": "Yes", "audience_explanation": "TMLR's audience from robot learning and reinforcement leraning may be interested in this paper. However, the paper is quite application-driven, focusing heavily on robotics engineering and system integration, which might be more typical for robotics or control-focused venues. If the main contribution lies in software infrastructure and benchmarking rather than new RL algorithms or fundamental ML theory, some might see it as borderline for TMLR’s primary ML research scope.", "requested_changes": "- Please clarify the specific contributions to reinforcement learning or machine learning beyond software infrastructure.\n- Provide more details on the additional work and innovations compared to IsaacLab, especially focusing on RL/ML aspects and unique challenges addressed during training.\n- Include more experiments that demonstrate the unique benefits and necessity of this unified framework. Specifically, identify problem settings or tasks where this framework is the only viable option, as training across multiple platforms individually may be inconvenient but not a critical barrier.", "broader_impact_concerns": "no", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Elr9U3gWEx", "reviewer": "Reviewer_hTAR", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Summary: This paper proposes a multi-domain framework that enables the training, evaluation, and deployment of RL-based navigation policies across diverse robotic platforms and operational environments. It emphasizes the modular decoupling of robots and tasks. The authors verified the simulation training and real-world transfer capabilities of the policy on three platforms: the ground-based Turtlebot2, the surface-based Kingfisher, and the microgravity Floating Platform.\n\nStrengths:\n(1)The paper is well-structured and clearly organized, making it easy to follow the motivation, methodology, and experimental validation.\n(2)The modular design is effective, as the framework decouples robots and tasks while supporting customizable dynamics and reward shaping. This design enables strong generalization and easy extensibility.\n(3)The paper provides a lightweight, Dockerized deployment API with ROS2 integration, allowing policies trained in IsaacLab to be efficiently deployed on physical platforms. This demonstrates strong practical applicability.\n\nWeaknesses:\n(1)The selected tasks focus primarily on low-level control in obstacle-free environments, which limits the demonstration of RoboRAN’s extensibility to more complex scenarios such as visual perception or dynamic obstacle avoidance.\n(2)Although the paper incorporates a domain randomization mechanism, it lacks a systematic ablation study to evaluate its effectiveness. For example, by comparing performance with and without different types of perturbations.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper presents extensive simulation and real-world experiments across multiple robots and tasks, with detailed quantitative metrics. The modular design and sim-to-real transfer are well-demonstrated. However, the impact of domain randomization lacks ablation analysis, which slightly weakens the robustness claim.", "audience_yn": "Yes", "audience_explanation": "The paper addresses key challenges in reinforcement learning for robotics, namely generalization, sim-to-real transfer, and modular design. This is highly relevant to both academic researchers in embodied AI.", "requested_changes": "(1) Ablation Study on Domain Randomization. The framework claims robustness via domain randomization, but no systematic analysis is provided to quantify its impact.\n(2) Evaluation in More Complex Environments (e.g., with obstacles or perceptual inputs).", "broader_impact_concerns": "I do not identify any immediate ethical concerns arising from this work.", "additional_comments": "Please refer to the detailed comments above.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)