arXiv:2606.22062v2 Announce Type: replace-cross Abstract: Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynamics is still poorly understood. In this work, we focused on the allocation of real-robot measurement time between system identification and domain randomization. We studied this tradeoff in a controlled sim-to-sim pendulum setting, where a hidden-parameter model stands in for the physical robot, and the experiment swee
The increasing sophistication and widespread application of robotic systems necessitate more efficient and robust methods for deploying learned policies from simulation to reality.
Optimizing sim-to-real transfer is crucial for accelerating the development and deployment of advanced robotics, impacting industries from logistics to manufacturing.
This research provides a framework for intelligently allocating real-robot measurement resources, potentially leading to faster and more reliable robot training strategies.
- · Robotics companies
- · AI research labs
- · Automation industries
- · Companies with inefficient robotics development cycles
More efficient robot learning and deployment through optimized resource allocation between system identification and domain randomization.
Faster commercialization and broader adoption of intelligent robotic systems in various sectors.
The development of highly adaptive and versatile robots capable of operating effectively in novel, unstructured environments with minimal real-world training.
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