Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World¶
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017). Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 23-30.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Idealized-Substrate Fallacy
- The same structure governs reinforcement-learning sim-to-real transfer: the simulator is the idealized substrate (perfect actuation, no sensor noise, no latency), the robot hardware is the real substrate, and a policy that flies in sim stalls on the elided actuation-noise and latency terms — fixed by domain randomisation and latency modelling, which is the substrate audit re-staging each dropped term.
This sourceAddresses the sim-to-real gap — simulators elide actuation noise, sensor noise, and latency — by re-introducing those terms via domain randomization.
- The same structure governs reinforcement-learning sim-to-real transfer: the simulator is the idealized substrate (perfect actuation, no sensor noise, no latency), the robot hardware is the real substrate, and a policy that flies in sim stalls on the elided actuation-noise and latency terms — fixed by domain randomisation and latency modelling, which is the substrate audit re-staging each dropped term.
- Training Serving Skew
- Machine-learning operations (canonical): a model trained on one period's data deployed against a later, shifted distribution; or training- and serving-time feature pipelines that process inputs differently, so the model receives different inputs at serving than at training. Sim-to-real robotics: a controller trained in a physics simulator whose missing details — friction, sensor noise, latency — cause systematic failures on the physical robot.
This sourceIntroduces domain randomization as a remedy for the sim-to-real gap in robot control.
- Machine-learning operations (canonical): a model trained on one period's data deployed against a later, shifted distribution; or training- and serving-time feature pipelines that process inputs differently, so the model receives different inputs at serving than at training. Sim-to-real robotics: a controller trained in a physics simulator whose missing details — friction, sensor noise, latency — cause systematic failures on the physical robot.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:23089ef1f4e3 · see in the full table