Pith. sign in

REVIEW 4 major objections 5 minor 61 references

Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A low-cost, open-source wheeled-robot ecosystem built on Isaac Lab achieves zero-shot sim-to-real transfer for drifting, elevation traversal, and visual navigation.

desk verdict Worth reading for the open-source ecosystem, but the 'first zero-shot drift' claim overreaches the evidence. read the letter →

arxiv 2502.07380 v2 pith:CPBS3KET submitted 2025-02-11 cs.RO

Tyler Han , Preet Shah , Sidharth Rajagopal , Yanda Bao , Sanghun Jung , Sidharth Talia , Gabriel Guo , Bryan Xu
show 4 more authors
Bhaumik Mehta Emma Romig Rosario Scalise Byron Boots
This is my paper · ORCID
classification cs.RO
keywords sim-to-realtransferreinforcementlearningdomainrandomizationautonomousdriftingelevationmapnavigationvisuallow-costroboticsopen-source
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Wheeled Lab connects inexpensive, open-source wheeled robots to a modern, GPU-accelerated robot-learning simulator and claims that this combination is enough to train policies in simulation that transfer directly to real hardware with no real-world fine-tuning. The headline demonstration is the first zero-shot transfer of a drifting policy: a small RC car trained entirely in simulation completes drifting laps on indoor carpet, with a maximum controlled side-slip angle of about 58 degrees and an average speed near 1.6 m/s. The same pipeline also yields an elevation policy that uses a local 2.5 by 2.5 meter elevation map to distinguish walls from traversable ramps, and a visual policy that follows a white figure-eight path on a black floor from grayscale camera images. If the paper is right, the expensive, specialist tooling of modern Sim2Real—massive parallelization, domain randomization, sensor simulation—can fit on roughly $900 to $3,000 platforms and be placed in the hands of classrooms and hobbyists.

What carries the argument

The central object is the Wheeled Lab training stack, which extends Isaac Lab with modular run, agent, and environment configurations so that scene, observation, reward, and hyperparameter choices are bundled and reproducible. The policies are trained with PPO in massively parallel simulation (1,024 parallel agents for drift and elevation; over 2,000 for visual map generation) using domain randomization across friction, actuator gains, and visual appearance, plus perturbation simulation and observation corruption. Drifting also required physical changes to the platform—tape on the tires and a rear-wheel-drive conversion—and a friction estimate made with a cheap spring scale (about 0.4), used as the center of the randomization; several simulation-to-simulation cycles narrowed the actuator gain ranges. Hand-designed rewards carry much of the task: the drifting reward includes cross-track distance, speed, side-slip stability, progress, turn energy, and a "turn-left-go-right" term that encourages the policy to discover counter-steering as an alternate mode, while the visual policy uses velocity and traversability rewards plus image augmentation such as color jittering and Gaussian blur.

What would settle it

Take the released open-source drifting stack, set it up on a second unit of the same platform without any retraining, and run it on a different indoor surface whose friction is measured with the same spring-scale method to be below the 0.4 training midpoint (say 0.3). The paper's zero-shot claim predicts the policy should still complete laps reliably; if it consistently spins out and fails to finish a lap, the claim would be falsified.

Watch

Extended reading notes

Core claim

The central claim is that a coherent, reproducible training stack—built on the Isaac Lab simulator and organized into run, agent, and environment abstractions—can close the sim-to-real gap for three distinct tasks on low-cost wheeled platforms. For drifting, the paper claims the first direct Sim2Real transfer that requires no online fine-tuning: the policy learns to cut throttle, steer sharply inward to throw out the rear wheels, then counter-steer while throttling through the turn, and it keeps full laps at about 1.6 m/s with controlled slip angles reaching 58 degrees. For elevation, the policy learns to use a local body-centric elevation map to approach and climb ramps while avoiding walls, something the baseline policy trained without parallelization, perturbation, and domain randomization cannot do reliably. For vision, the paper shows that a grayscale 40 by 60 pixel observation with aggressive image augmentation transfers to a real white-on-black figure-eight course, whereas policies without augmentation fail; in five trials the best configuration succeeds three times. These are stated as demonstrations of a pipeline, not as guarantees of performance on all surfaces or all hardware.

Load-bearing premise

The central claim depends on the assumption that the Isaac Lab simulation, together with hand-designed rewards and coarse physical measurements (a spring-scale friction estimate of about 0.4 and actuator gains taken from datasheets and narrowed in simulation-to-simulation cycles), models the real HOUND and MuSHR platforms faithfully enough that policies transfer to real hardware with no real-world policy updates.

Editorial extensions

If this is right

  • Agile driving research becomes accessible to budget-constrained labs, since a drifting policy can be trained in simulation and deployed on a roughly $3,000 RC car without real-world fine-tuning.
  • Elevation-based traversability reasoning, previously standard for legged robots, can be trained end-to-end on wheeled platforms using local elevation maps and transferred to real ramps and obstacles.
  • Camera-based reinforcement learning becomes approachable with simple grayscale observations and aggressive image augmentation, lowering the data and rendering demands of visual Sim2Real.
  • Classrooms and hobbyists can learn the full modern robotics loop—designing rewards, training in parallel simulation, deploying on hardware, and iterating—on open-source platforms rather than proprietary or expensive ones.
  • The strong baseline failures indicate that earlier low-fidelity wheeled-robot ecosystems omit the very techniques (massive parallelization, perturbation, domain randomization) that make modern Sim2Real work.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural testable extension is to run the same training recipe on other open-source wheeled platforms, such as an F1Tenth-style car, to see whether the framework's platform-agnostic abstractions generalize as the paper's design implies.
  • The spring-scale friction measurement and the several simulation-to-simulation gain-narrowing cycles suggest that this zero-shot pipeline is not fully parameter-free; a likely next step, which the paper names as robust adaptation, is online adjustment of friction and actuator parameters on more varied surfaces.
  • The grayscale simplification strategy suggests a testable design principle: reducing sensor complexity may beat increasing rendering fidelity for visual Sim2Real on structured tasks, and comparing the current grayscale pipeline to a photorealistic RGB pipeline on the same figure-eight course would settle this.
  • Because the paper's educational claim is not yet backed by a user study, a controlled classroom evaluation of how quickly novices can go from simulation to deployment would be the natural way to test the accessibility argument.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents Wheeled Lab, an open-source ecosystem that integrates low-cost wheeled robots (HOUND and MuSHR) with Isaac Lab for reinforcement-learning-based Sim2Real research and education. The authors contribute modular environment and configuration abstractions, then demonstrate three learned policies trained with PPO in Isaac Lab and deployed on real hardware: aggressive drifting, elevation traversal, and visual navigation. The manuscript includes ablations over domain randomization, perturbations, parallelization, architecture, and image augmentation, and it makes a central novelty claim that this is the first direct Sim2Real transfer for drifting without online fine-tuning.

Significance. If the claims hold, Wheeled Lab would be a valuable community resource: it lowers the cost barrier to modern Sim2Real methods, provides reproducible baseline configurations for accessible platforms, and demonstrates end-to-end pipelines for three distinct tasks. The paper's concrete strengths are the breadth of the integrated hardware/software stack, the comparative survey of existing ecosystems, and the public release of code, configurations, and videos. However, the quantitative evaluation is currently too thin to support the strongest novelty claims, and the appendix documents real-world calibration steps that are downplayed in the main text. The infrastructure contribution is solid; the 'first zero-shot drifting' claim is the main risk.

major comments (4)
  1. [Section 1, Section 5, Appendix B.1.1-B.1.2] The claim of 'first zero-shot transfer' for drifting is not supported as stated. Appendix B.1.1 describes 'a few Sim2Real2Sim cycles were spent narrowing the randomized gain ranges for the throttle actuator settings,' and Appendix B.1.2 describes a 'long thread of Sim2Sim cycles' plus a real spring-scale measurement used to set the friction randomization midpoint to about 0.4. These are real-world calibration and system-identification steps, and the Related Work paragraph explicitly says the authors distance their methods from 'gain tuning and extensive system identification.' The term 'zero-shot' therefore needs a precise definition, and the claim should be softened to something like 'without online policy fine-tuning after simulator calibration,' or the paper must show that no real-world information entered policy training. Section 6's admission that the released policies 'must be met with patience and an intent to iterate' further weakens the out-of-the-box zero-shot framing.
  2. [Section 4.1] The drift experiments lack quantitative trial-level statistics. The text reports complete laps, a maximum controlled slip angle of 58 degrees, and an average speed of about 1.6 m/s, but it gives no number of trials, success rate, failure count, or a precise definition of 'complete laps.' The setup also says that motion capture is used for evaluation and occasional 1 Hz integrator-drift correction, while successful VIO-only runs are only shown on the website and are 'not used for data collection.' The reported deployment is therefore not demonstrated under the stated onboard state-estimation conditions, and the 'first' claim cannot be distinguished from a policy tuned into existence. Please report trial counts and success/failure rates, and either use onboard VIO for the reported runs or explicitly acknowledge the motion-capture assistance in the main-text results.
  3. [Section 4.3, Table 3] The quantitative visual-policy comparison rests on five trials per setting. The conclusion that image augmentation is 'essential' is supported by 0/5 versus 3/5 and 0/5 versus 1/5, but the CNN-versus-MLP generalization conclusion (3/5 versus 1/5) is within the range of random variation; no confidence intervals, per-trial descriptions, or failure-mode analyses are given. Additionally, linear and angular velocities are obtained from the motion capture system, so the policy is not evaluated under fully onboard state estimation. More trials or a statistical treatment is needed before drawing architecture-level conclusions.
  4. [Section 4.2, Figure 6] The elevation policy evaluation is qualitative only. The text states that the baseline 'primarily deviates from any elevation features' and that the trained policy 'can both traverse the ramp safely and navigate through subsequent obstacles,' but no trial counts, success rates, or failure statistics are reported. Since elevation traversal is one of the three headline demonstrations, quantitative evaluation should be added or the claims should be scaled back to match the evidence.
minor comments (5)
  1. [Section B.1.4] The 'Turn-Left-Go-Right' reward explicitly rewards counter-steering when the angular velocity and steering command are opposite, so the drifting behavior is partly shaped by the reward design; the main text should acknowledge this when describing the behavior as discovered rather than engineered.
  2. [Section 4.1] The sentence 'Successful VIO-only runs not used for data collection' is missing punctuation and is easy to misread; please clarify whether the VIO-only runs were used for qualitative demonstration only.
  3. [Table 3] The header '# of Success / Trial' is ambiguous; it should read '# successes out of 5 trials' for each condition.
  4. [Algorithm 1] Lines 18-22 use a non-standard 'Do ... do While' pseudo-code structure; a conventional repeat-until loop with an explicit condition would be clearer.
  5. [References] References [39]-[43] contain incomplete bibliographic entries, with missing publication dates and venues; please complete them.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical systems contribution; the 'zero-shot' claim is weakened by disclosed Sim2Real2Sim tuning and friction measurement, but no derivation reduces to its own inputs.

full rationale

This is an empirical systems paper, not a formal derivation, so the circularity analysis focuses on whether any claimed result is equivalent to its inputs by construction. The central claims are the three deployed policies and the 'first zero-shot drift' headline. No equation or fitted parameter is presented whose output is the conclusion by construction. The drifting reward includes shaped terms (Side-Slip, Turn-Left-Go-Right in Appendix B.1.4) that explicitly reward counter-steering and stable side-slip; this means the learned maneuver is partially induced by the reward, but the policy still has to discover and stabilize the dynamics, and the paper openly documents these rewards rather than presenting them as emergent predictions. The more serious issue is that the 'zero-shot' label is strained by the paper's own appendix: B.1.1 admits 'a few Sim2Real2Sim ... cycles were spent narrowing the randomized gain ranges for the throttle actuator settings,' and B.1.2 describes measuring friction with a spring scale on the deployment carpet and using it as the randomization midpoint, while Section 6 cautions that the released policies 'must be met with patience and an intent to iterate.' Those admissions weaken the zero-shot claim as an evidentiary matter, but they are real-world system identification and iterative debugging, not a circular reduction: the deployed policy is not the same object as the measured friction value, and success on the task is not logically entailed by the fitted parameters. Self-citations (HOUND [1], MuSHR [2], and the authors' earlier RL/off-road work) are used to document hardware and prior art, not as an unverified premise that forces the conclusion. No uniqueness theorem is imported from the authors, and no known result is merely renamed. Verdict: no significant circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on empirical sim-to-real transfer rather than on a mathematical derivation, so the ledger captures the hand-set physical parameters, reward choices, and modeling assumptions that make the demonstrations work. No new physical entities are introduced.

free parameters (4)
  • friction coefficient midpoint = ~0.4
    Measured with a cheap spring scale and used as midpoint of the domain randomization range; critical for drift transfer (Appendix B.1.2).
  • reward weights = not reported
    Hand-designed relative weights (e.g., cross-track distance 'highly weighted', side-slip only stable angles, turn-left-go-right shaping) define the task objectives (Appendices B.1.4, B.2.3, B.3.4).
  • domain randomization ranges = friction 0.2-0.8 initially, narrowed; gain ranges narrowed
    Ranges tuned through Sim2Real2Sim cycles, affecting policy transfer (Appendices B.1.1, B.1.2).
  • image augmentation parameters = aggressive color jitter, Gaussian blur (not quantified)
    Required for visual sim2real transfer; only qualitative settings given (Appendix B.3.3).
assumptions (4)
  • domain assumption Isaac Lab simulation is a sufficiently faithful model of the HOUND and MuSHR platforms for zero-shot policy transfer.
    The entire zero-shot claim depends on simulator fidelity; tested empirically but not proven across conditions (Section 4).
  • domain assumption Motion capture provides ground-truth state estimates used in evaluation and in visual policy observations.
    Visual policy velocities are read from motion capture, and drift evaluation uses 1 Hz integrator correction; onboard VIO-only runs are only shown on the website (Sections 4.1, 4.3).
  • domain assumption Grayscale black/white tile task is a representative visual navigation task.
    The task simplifies visual semantics to color contrast; generalization to richer scenes is untested (Section 4.3).
  • domain assumption Hand-designed reward functions capture the intended task objectives.
    Rewards are engineered to incentivize desired behaviors (e.g., turn-left-go-right to discover drifting); no analysis of reward misspecification (Appendix B).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics." pith.science (2026). https://pith.science/paper/CPBS3KET

@misc{pith2026250207380,
  author       = {Pith},
  title        = {Pith review of: Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CPBS3KET}},
  note         = {Machine review of arXiv:2502.07380}
}
read the original abstract

Reinforcement Learning (RL) has been pivotal in recent robotics milestones and is poised to play a prominent role in the future. However, these advances can rely on proprietary simulators, expensive hardware, and a daunting range of tools and skills. As a result, broader communities are disconnecting from the state-of-the-art; education curricula are poorly equipped to teach indispensable modern robotics skills involving hardware, deployment, and iterative development. To address this gap between the broader and scientific communities, we contribute Wheeled Lab, an ecosystem which integrates accessible, open-source wheeled robots with Isaac Lab, an open-source robot learning and simulation framework, that is widely adopted in the state-of-the-art. To kickstart research and education, this work demonstrates three state-of-the-art zero-shot policies for small-scale RC cars developed through Wheeled Lab: controlled drifting, elevation traversal, and visual navigation. The full stack, from hardware to software, is low-cost and open-source. Videos and additional materials can be found at: https://uwrobotlearning.github.io/WheeledLab/

Figures

Figures reproduced from arXiv: 2502.07380 by the authors.

Figure 1
Figure 1. Wheeled Lab bridges popular low-cost open-source wheeled platforms [ [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Modular training framework imagined as the assembly of a puzzle. Three main components comprise training: Run, Agent, Environment. Outlined puzzle pieces (e.g., Observation, Reward) are sub-components whose behaviors are defined by the pieces below them. For instance, High Speed is a reward setting in all of our RL environments, as denoted by the star ( ) icon. Wheel ( ), camera ( ), and mountain ( ) icons indicate … view at source ↗
Figure 3
Figure 3. Several autonomy platforms shared among stu￾dents in a robotics class. While we contribute the novelties in applied RL as discussed in Section 1, our experiments aim to highlight the disparity in open￾source methodologies between the broader and scientific commu￾nities. Thus, we compare policies trained through Wheeled Lab to baselines which reflect what training ecosystems currently provide to low-cost wheeled plat… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: A control strategy arises for drifting through turns. Left: (Overlay: colored trajectories over multiple runs.) One run is spotlighted for this visualization. Arrows indicate the direction of travel, and the colorbar denotes speeds (in m/s). Middle: Magnification of dr…
Figure 5
Figure 5. Figure 5: Captured trajectories from the base￾line drift policy in real experiments. The base￾line is unable to complete a valid turn without crashing or spinning out. Background. Drifting is characterized by a ve￾hicle’s side-slip angle, i.e., the angle between the heading and …
Figure 6
Figure 6. Figure 6: Comparison of policy πelev and baseline πelev in simulation and real-world elevation tasks. Fig. 6a shows real-world execution: πelev(top) successfully avoids obstacles using local elevation, while πelev (bottom) avoids elevation features entirely. Fig. 6b visualizes t…
Figure 7
Figure 7. Figure 7: Captured trajectories of visual policy in real experiments. The trajectories are obtained from the motion capture system. We include videos of each setting and trial in the supplementary. # Walkers = 1 # Walkers = 3 # Walkers = 5 [PITH_FULL_IMAGE:figures/full_fig_p007…
Figure 8
Figure 8. Figure 8: Examples of randomly generated en￾vironments to train πvis. Black and white rep￾resent penalizing and safe regions, respectively. We adjust the number of walkers to train the pol￾icy across different difficulties. Using a smaller number of walkers will make the trainin…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

61 extracted references · 43 canonical work pages

  1. [1]

    Demonstrating HOUND: A Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving

    S. Talia, M. Schmittle, A. Lambert, A. Spitzer, C. Mavrogiannis, and S. S. Srinivasa. Demonstrating HOUND: A Low-cost Research Platform for High-speed Off-road Underactu- ated Nonholonomic Driving, July 2024. URL http://arxiv.org/abs/2311.11199. arXiv:2311.11199 [cs]

    work page Pith review arXiv 2024

  2. [2]

    S. S. Srinivasa, P. Lancaster, J. Michalove, M. Schmittle, C. Summers, M. Rockett, R. Scalise, J. R. Smith, S. Choudhury, C. Mavrogiannis, and F. Sadeghi. MuSHR: A Low-Cost, Open- Source Robotic Racecar for Education and Research, Dec. 2023. URL http://arxiv. org/abs/1908.08031. arXiv:1908.08031 [cs]

    arXiv 2023

  3. [3]

    O’Kelly, H

    M. O’Kelly, H. Zheng, D. Karthik, and R. Mangharam. F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning. In Proceed- ings of the NeurIPS 2019 Competition and Demonstration Track , pages 77–89. PMLR, Aug. 2020. URL https://proceedings.mlr.press/v123/o-kelly20a.html. ISSN: 2640-3498

    work page 2019

  4. [4]

    Mittal, C

    M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y . Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. IEEE Robotics and Automation Letters, 8(6):3740–3747, June 2023. ISSN 2377-3766, 2377-3774. doi:10.1109/LRA.2023. 3270...

    arXiv 2023

  5. [5]

    T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V . Koltun, and M. Hutter. Learning robust per- ceptive locomotion for quadrupedal robots in the wild.Science Robotics, 7(62):eabk2822, Jan

    work page

  6. [6]

    Hoeller, N

    D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal parkour: Learning agile nav- igation for quadrupedal robots. Science Robotics , 9(88):eadi7566, Mar. 2024. doi:10. 1126/scirobotics.adi7566. URL https://www.science.org/doi/full/10.1126/ scirobotics.adi7566. Publisher: American Association for the Advancement of Sci- ence

    work page 2024

  7. [7]

    Kaufmann, L

    E. Kaufmann, L. Bauersfeld, A. Loquercio, M. M ¨uller, V . Koltun, and D. Scaramuzza. Champion-level drone racing using deep reinforcement learning. Nature, 620(7976):982– 987, Aug. 2023. ISSN 1476-4687. doi:10.1038/s41586-023-06419-4. URL https: //www.nature.com/articles/s41586-023-06419-4 . Publisher: Nature Pub- lishing Group

    doi:10.1038/s41586-023-06419-4 2023

  8. [8]

    Akkaya, M

    OpenAI, I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, J. Schneider, N. Tezak, J. Tworek, P. Welinder, L. Weng, Q. Yuan, W. Zaremba, and L. Zhang. Solving Rubik’s Cube with a Robot Hand, Oct

    work page

Show all 61 references
  1. [9]

    Huang, R

    K. Huang, R. Rana, A. Spitzer, G. Shi, and B. Boots. DATT: Deep Adaptive Trajectory Track- ing for Quadrotor Control, Dec. 2023. URL http://arxiv.org/abs/2310.09053. arXiv:2310.09053 [cs]

    2023 arXiv

  2. [10]

    Q. Liao, B. Zhang, X. Huang, X. Huang, Z. Li, and K. Sreenath. Berkeley Humanoid: A Research Platform for Learning-based Control, July 2024. URL http://arxiv.org/ abs/2407.21781. arXiv:2407.21781 [cs]

    2024 arXiv

  3. [11]

    T. V . Samak, C. V . Samak, S. Kandhasamy, V . Krovi, and M. Xie. AutoDRIVE: A Compre- hensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education. Robotics, 12(3):77, May 2023. ISSN 2218-6581. doi:10.3390/ 10 robotics1203007...

    2023 arXiv

  4. [12]

    Williams, N

    G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information theoretic MPC for model-based reinforcement learning. In 2017 IEEE Interna- tional Conference on Robotics and Automation (ICRA) , pages 1714–1721, May 2017. doi: 10.1109/ICRA...

    2017

  5. [13]

    T. Han, A. Liu, A. Li, A. Spitzer, G. Shi, and B. Boots. Model Predictive Control for Aggressive Driving Over Uneven Terrain, June 2024. URLhttp://arxiv.org/abs/2311.12284. arXiv:2311.12284 [cs]

    2024 arXiv

  6. [14]

    T. Han, S. Talia, R. Panicker, P. Shah, N. Jawale, and B. Boots. Dynamics Models in the Ag- gressive Off-Road Driving Regime, May 2024. URL http://arxiv.org/abs/2405. 16487. arXiv:2405.16487 [cs]

    2024 arXiv

  7. [15]

    Datar, C

    A. Datar, C. Pan, M. Nazeri, and X. Xiao. Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 16322–16329, May 2024. doi:10.1109/ICRA57147. 2024.10610079....

    2024

  8. [16]

    Horv ´ath, G

    E. Horv ´ath, G. Ign ´eczi, N. Mark ´o, R. Krecht, and M. Unger. Teaching Aspects of ROS 2 and Autonomous Vehicles. Engineering Proceedings, 79(1):49, 2024. ISSN 2673-4591. doi: 10.3390/engproc2024079049. URL https://www.mdpi.com/2673-4591/79/1/49. Number: 1 Publisher: Multidi...

    2024 doi

  9. [17]

    B. D. Evans, R. Trumpp, M. Caccamo, F. Jahncke, J. Betz, H. W. Jordaan, and H. A. Engel- brecht. Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks, Apr

  10. [18]

    Dosovitskiy, G

    A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun. CARLA: An Open Ur- ban Driving Simulator. In Proceedings of the 1st Annual Conference on Robot Learn- ing, pages 1–16. PMLR, Oct. 2017. URL https://proceedings.mlr.press/v78/ dosovitskiy17a.html. ISSN: 2640-3498

    2017

  11. [19]

    Brunnbauer, L

    A. Brunnbauer, L. Berducci, A. Brandst ¨atter, M. Lechner, R. Hasani, D. Rus, and R. Grosu. Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing, Feb. 2022. URL http://arxiv.org/abs/2103.04909. arXiv:2103.04909 [cs]

    2022 arXiv

  12. [20]

    Hamilton, P

    N. Hamilton, P. Musau, D. M. Lopez, and T. T. Johnson. Zero-Shot Policy Transfer in Autonomous Racing: Reinforcement Learning vs Imitation Learning. In 2022 IEEE In- ternational Conference on Assured Autonomy (ICAA) , pages 11–20, Mar. 2022. doi:10. 1109/ICAA52185.2022.00011. ...

    2022

  13. [21]

    X. Cai, J. Queeney, T. Xu, A. Datar, C. Pan, M. Miller, A. Flather, P. R. Osteen, N. Roy, X. Xiao, and J. P. How. PIETRA: Physics-Informed Evidential Learning for Traversing Out- of-Distribution Terrain. IEEE Robotics and Automation Letters , pages 1–8, 2025. ISSN 2377-3766. d...

    2025

  14. [22]

    Deitke, W

    M. Deitke, W. Han, A. Herrasti, A. Kembhavi, E. Kolve, R. Mottaghi, J. Salvador, D. Schwenk, E. VanderBilt, M. Wallingford, L. Weihs, M. Yatskar, and A. Farhadi. RoboTHOR: An Open Simulation-to-Real Embodied AI Platform. In 2020 IEEE/CVF Conference on Com- puter Vision and Pat...

    2020

  15. [23]

    Balaji, S

    B. Balaji, S. Mallya, S. Genc, S. Gupta, L. Dirac, V . Khare, G. Roy, T. Sun, Y . Tao, B. Townsend, E. Calleja, S. Muralidhara, and D. Karuppasamy. DeepRacer: Au- tonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learn- ing. In 2020 IEEE International Co...

    2020

  16. [24]

    Salimpour, J

    S. Salimpour, J. Pe ˜na-Queralta, D. Paez-Granados, J. Heikkonen, and T. Westerlund. Sim- to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 Robots, Jan. 2025. URL http://arxiv.org/abs/2501. 02902. arXiv:2501.02902 [cs]

    2025 arXiv

  17. [25]

    Samak, T

    C. Samak, T. Samak, and V . Krovi. Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem. IFAC-PapersOnLine, 56(3):277–282, Jan. 2023. ISSN 2405-

    2023

  18. [26]

    W. Xiao, H. Xue, T. Tao, D. Kalaria, J. M. Dolan, and G. Shi. AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility, Sept. 2024. URL http:// arxiv.org/abs/2409.15783. arXiv:2409.15783 [cs]

    2024 arXiv

  19. [27]

    Cutler and J

    M. Cutler and J. P. How. Autonomous drifting using simulation-aided reinforcement learning. In 2016 IEEE International Conference on Robotics and Automation (ICRA) , pages 5442– 5448, Stockholm, Sweden, May 2016. IEEE. ISBN 978-1-4673-8026-3. doi:10.1109/ICRA. 2016.7487756. UR...

    2016

  20. [28]

    B. D. Evans, H. W. Jordaan, and H. A. Engelbrecht. Comparing deep reinforcement learning architectures for autonomous racing. Machine Learning with Applications , 14:100496, Dec

  21. [29]

    Gonzales, F

    J. Gonzales, F. Zhang, K. Li, and F. Borrelli. Autonomous drifting with onboard sensors. In Advanced Vehicle Control. CRC Press, 2016. ISBN 978-1-315-26528-5. Num Pages: 6

    2016

  22. [30]

    P. Cai, X. Mei, L. Tai, Y . Sun, and M. Liu. High-Speed Autonomous Drifting With Deep Reinforcement Learning. IEEE Robotics and Automation Letters, 5(2):1247–1254, Apr. 2020. ISSN 2377-3766. doi:10.1109/LRA.2020.2967299. URL https://ieeexplore.ieee. org/document/8961997/?arnum...

    2020

  23. [31]

    Djeumou, M

    F. Djeumou, M. Thompson, M. Suminaka, and J. Subosits. Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies, Oct. 2024. URL http://arxiv.org/abs/2410.20990. arXiv:2410.20990 [cs]

    2024 arXiv

  24. [32]

    G. Chen, X. Zhao, Z. Gao, and M. Hua. Dynamic Drifting Control for General Path Tracking of Autonomous Vehicles. IEEE Transactions on Intelligent Vehicles , 8(3):2527–2537, Mar

  25. [33]

    J. Frey, M. Patel, D. Atha, J. Nubert, D. Fan, A. Agha, C. Padgett, P. Spieler, M. Hutter, and S. Khattak. RoadRunner – Learning Traversability Estimation for Autonomous Off-road Driving, Aug. 2024. URL http://arxiv.org/abs/2402.19341. arXiv:2402.19341 [cs]. 12

    2024 arXiv

  26. [34]

    Gibson, B

    J. Gibson, B. Vlahov, D. Fan, P. Spieler, D. Pastor, A.-a. Agha-mohammadi, and E. A. Theodorou. A Multi-step Dynamics Modeling Framework For Autonomous Driving In Multiple Environments, May 2023. URL http://arxiv.org/abs/2305.02241. arXiv:2305.02241 [cs]

    2023 arXiv

  27. [35]

    Stachowicz, D

    K. Stachowicz, D. Shah, A. Bhorkar, I. Kostrikov, and S. Levine. FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing. In Proceedings of The 7th Conference on Robot Learning, pages 3100–3111. PMLR, Dec. 2023. URL https: //proceedings.mlr.pre...

    2023

  28. [36]

    T. Xu, C. Pan, and X. Xiao. Reinforcement Learning for Wheeled Mobility on Ver- tically Challenging Terrain. In 2024 IEEE International Symposium on Safety Se- curity Rescue Robotics (SSRR) , pages 125–130, Nov. 2024. doi:10.1109/SSRR62954. 2024.10770034. URL https://ieeexplor...

    2024

  29. [37]

    K. Kang, S. Belkhale, G. Kahn, P. Abbeel, and S. Levine. Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Au- tonomous Flight. In 2019 International Conference on Robotics and Automation (ICRA), pages 6008...

    2019

  30. [38]

    Z. Yuan, T. Wei, S. Cheng, G. Zhang, Y . Chen, and H. Xu. Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning, Oct. 2024. URL http: //arxiv.org/abs/2407.15815. arXiv:2407.15815 [cs]

    2024 arXiv

  31. [39]

    doi:10.1109/TIV .2023.3235007

    ISSN 2379-8904. doi:10.1109/TIV .2023.3235007. URL https://ieeexplore. ieee.org/document/10011537/?arnumber=10011537. Conference Name: IEEE Transactions on Intelligent Vehicles

    2023

  32. [40]

    URL https://agilityrobotics.com/content/ crossing-sim2real-gap-with-isaaclab

    Crossing the Sim2Real Gap With NVIDIA Isaac Lab, . URL https://agilityrobotics.com/content/ crossing-sim2real-gap-with-isaaclab

  33. [41]

    URL https://support.bostondynamics.com/s/article/ Get-Started-with-Reinforcement-Learning-for-Spot-49966

    Get Started with Reinforcement Learning for Spot, . URL https://support.bostondynamics.com/s/article/ Get-Started-with-Reinforcement-Learning-for-Spot-49966

  34. [42]

    URL https://fieldai.com/news/ field-ai-nvidia-partnership

    NVIDIA Isaac Lab Blog, . URL https://fieldai.com/news/ field-ai-nvidia-partnership

  35. [43]

    URL https://menteebot.com/blog/ #shopping-companion-2024

    MenteeBot, . URL https://menteebot.com/blog/ #shopping-companion-2024

    2024

  36. [44]

    Y . J. Ma, W. Liang, G. Wang, D.-A. Huang, O. Bastani, D. Jayaraman, Y . Zhu, L. Fan, and A. Anandkumar. Eureka: Human-Level Reward Design via Coding Large Language Models, Apr. 2024. URL http://arxiv.org/abs/2310.12931. arXiv:2310.12931 [cs]

    2024 arXiv

  37. [45]

    S. Tao, F. Xiang, A. Shukla, Y . Qin, X. Hinrichsen, X. Yuan, C. Bao, X. Lin, Y . Liu, T.-k. Chan, Y . Gao, X. Li, T. Mu, N. Xiao, A. Gurha, Z. Huang, R. Calandra, R. Chen, S. Luo, and H. Su. ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embo...

    2024 arXiv

  38. [46]

    Technologies

    X. Technologies. 1X Technologies | Safe, Intelligent Humanoids. URL https://www.1x. tech/

  39. [47]

    T. Han, Y . Bao, B. Mehta, G. Guo, A. Vishwakarma, E. Kang, S. Jung, R. Scalise, J. Zhou, B. Xu, and B. Boots. Model Predictive Adversarial Imitation Learning for Planning from Ob- servation, July 2025. URL http://arxiv.org/abs/2507.21533. arXiv:2507.21533 [cs]

    2025

  40. [48]

    Schulman, F

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal Policy Op- timization Algorithms, Aug. 2017. URL http://arxiv.org/abs/1707.06347. arXiv:1707.06347

    2017 arXiv

  41. [49]

    J. Lee, J. Hwangbo, L. Wellhausen, V . Koltun, and M. Hutter. Learning quadrupedal lo- comotion over challenging terrain. Science Robotics , 5(47):eabc5986, Oct. 2020. doi: 10.1126/scirobotics.abc5986. URL https://www.science.org/doi/10.1126/ scirobotics.abc5986. Publisher: Am...

    2020 doi

  42. [50]

    Acosta, S

    M. Acosta, S. Kanarachos, and M. E. Fitzpatrick. A Hybrid Hierarchical Rally Driver Model for Autonomous Vehicle Agile Maneuvering on Loose Surfaces:. In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics , pages 216–225, Madrid...

    2017 doi

  43. [51]

    S. Jung, J. Lee, X. Meng, B. Boots, and A. Lambert. V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation. In 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages 1766–1773, May 2024. doi:10.1109/ICRA57147. 2024.10611227. URL ...

    2024

  44. [52]

    Y . D. V . Yasuda, L. E. G. Martins, and F. A. M. Cappabianco. Autonomous Visual Navigation for Mobile Robots: A Systematic Literature Review. ACM Computing Surveys, 53(1):1–34, Jan. 2021. ISSN 0360-0300, 1557-7341. doi:10.1145/3368961. URL https://dl.acm. org/doi/10.1145/3368961

    2021 doi

  45. [53]

    Y . Yang, G. Shi, C. Lin, X. Meng, R. Scalise, M. G. Castro, W. Yu, T. Zhang, D. Zhao, J. Tan, and B. Boots. Agile Continuous Jumping in Discontinuous Terrains, Sept. 2024. URL http://arxiv.org/abs/2409.10923. arXiv:2409.10923 [cs]. 13

    2024 arXiv

  46. [54]

    Sim2Real2Sim

    K. Bousmalis, A. Irpan, P. Wohlhart, Y . Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige, S. Levine, and V . Vanhoucke. Using Simulation and Do- main Adaptation to Improve Efficiency of Deep Robotic Grasping. In 2018 IEEE Interna- tional Conference ...

    2018

  47. [60]

    S. Choi, S. Jung, H. Yun, J. T. Kim, S. Kim, and J. Choo. RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening. pages 11580–11590, 2021. URL https://openaccess.thecvf.com/content/ CVPR2021/html/Choi_RobustNet_Improving_Domain_...

    2021

  48. [2019]

    arXiv:1910.07113 [cs]

    URL http://arxiv.org/abs/1910.07113. arXiv:1910.07113 [cs]

    1910 arXiv

  49. [2020]

    ISBN 978-1-72817-168-5

    IEEE. ISBN 978-1-72817-168-5. doi:10.1109/CVPR42600.2020.00323. URL https: //ieeexplore.ieee.org/document/9157346/. 11

    2020

  50. [2022]

    URL https://www.science.org/doi/abs/ 10.1126/scirobotics.abk2822

    doi:10.1126/scirobotics.abk2822. URL https://www.science.org/doi/abs/ 10.1126/scirobotics.abk2822. Publisher: American Association for the Advance- ment of Science

    doi

  51. [2023]

    doi:10.1016/j.mlwa.2023.100496

    ISSN 26668270. doi:10.1016/j.mlwa.2023.100496. URL https://linkinghub. elsevier.com/retrieve/pii/S266682702300049X

    2023

  52. [2024]

    arXiv:2402.18558 [cs]

    URL http://arxiv.org/abs/2402.18558. arXiv:2402.18558 [cs]

    arXiv

  53. [8963]

    URL https://www.sciencedirect.com/ science/article/pii/S2405896323023704

    doi:10.1016/j.ifacol.2023.12.037. URL https://www.sciencedirect.com/ science/article/pii/S2405896323023704

    2023 doi

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.