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Sim-to-real transfer has its strongest current evidence in bounded, robot-specific control tasks—especially legged locomotion. Local mobile-robot navigation also has a promising zero-shot demonstration. These results show that transfer can work under tested conditions; they do not establish broad reliability across unfamiliar hardware, changing environments, or long-term commercial operation.

Where real-world transfer has the clearest evidence

Task area What was tested What the evidence supports Important limit
Legged locomotion A 2026 study by Bjelonic, Tischhauser, and Hutter tested a framework on three primary platforms and deployed it on ten additional robots. The authors report cross-platform deployment without randomizing dynamic parameters. They also report a 32% reduction in the full Cost of Transport of ANYmal (1.27). This is a result for one framework and task family, not a general transfer rate or proof of fleet-scale operational reliability.
Local mobile-robot navigation A 2025 arXiv preprint trained policies in Isaac Sim, tested them through Gazebo, and deployed them on physical ROS 2 robots. The authors report zero-shot transfer and benchmark performance comparable to Nav2 in their evaluation. It is a preprint, and the result is limited to the reported tests. The authors note that exteroception-based methods are less standardized and reproducible than proprioception-based work.
Manipulation A 2024 IEEE survey reviews methods including domain randomization, domain adaptation, meta-learning, knowledge distillation, and policy-performance correlation. There is substantial methods research on transferring policies to manipulation tasks. The surveyed sources do not establish a common reliability ranking that would show manipulation is as mature as, or less mature than, locomotion or navigation.

The most concrete breadth in this evidence comes from the legged-robot study: deployment on ten additional robots is useful evidence that the framework was not confined to its primary platforms. It remains a count of experimental deployments, not a percentage of successful runs or a measure of reliability over months of operation. The reported 32% reduction is likewise the authors’ study-specific comparison, not a field-wide effect.

What “reliably” means in these demonstrations

Transfer is bounded by the tested robot and task

A policy may transfer well to a known robot performing a defined task while remaining untested on different actuators, unfamiliar surfaces, clutter, altered sensor conditions, or longer operating periods. A successful physical demonstration is stronger evidence than a high simulator score, but it does not automatically establish performance outside the conditions that were evaluated.

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Zero-shot and adapted results are different claims

Zero-shot transfer means a policy is deployed without real-world fine-tuning for the target evaluation; it is not the same as proving the policy never depends on robot-specific modeling or preparation. When comparing studies, check whether physical trials involved fine-tuning, calibration, parameter identification, or other adaptation. The navigation preprint explicitly reports zero-shot transfer; the locomotion study describes bottom-up dynamic parameter identification from actuators through whole-robot trajectories and on-ground locomotion.

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The simulator and deployment software both matter

Transfer is a property of the whole pipeline, not just the simulated environment. The 2026 Annual Review, The Reality Gap in Robotics: Challenges, Solutions, and Best Practices, by Elie Aljalbout and coauthors, emphasizes that aligning the software stack between simulation and physical deployment matters alongside physical modeling. The review characterizes closing the reality gap as “one of the most pressing challenges in robotics.” A convincing result therefore needs to account for how sensors, control interfaces, timing, and deployment software behave on the real robot, as well as how the robot’s dynamics are represented.

How to judge a sim-to-real result

When evaluating a paper or product claim, look for these details rather than relying on the phrase “sim-to-real” by itself:

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  • Task and platform: What exactly did the robot do, and which robot, actuators, and sensors were used?
  • Transfer protocol: Was the physical deployment zero-shot, or did it use real-world fine-tuning, calibration, or system identification?
  • Physical evaluation: Were results measured on hardware, or reported only in simulation? What baseline and metric were used?
  • Scope: How many robots, environments, surfaces, or sensor conditions were tested? Were these meaningfully different?
  • Observations: Did the policy rely on proprioception, cameras, or other exteroceptive inputs? The navigation preprint’s authors specifically flag weaker standardization and reproducibility for exteroception-based work.
  • Duration and operating conditions: How long was the physical evaluation, and does it resemble the conditions in which the system is supposed to operate?

These dimensions should remain separate. A study with more robot platforms does not necessarily test more environments; a zero-shot result does not itself demonstrate long-term stability; and a strong simulation benchmark does not substitute for physical validation.

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What the evidence does not establish

The 2026 Annual Review and a survey of real-world deep reinforcement learning describe meaningful progress alongside unresolved reality-gap challenges. The broader survey literature calls for stable, sample-efficient methods and principled development and evaluation procedures, and notes that reinforcement learning has not seen widespread industrial deployment. The sources summarized here do not establish a cross-domain percentage of sim-to-real policies that remain reliable in long-term commercial use, nor do they compare locomotion, navigation, and manipulation under one shared benchmark. Treat claims of general reliability or commercial readiness accordingly.

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