Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation
arXiv:2605.28812v1 Announce Type: cross Abstract: A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data. Sim-to-real reinforcement learning offers a scalable alternative, but the simulation-reality gap prevents information-dense modalities like touch from being effectively used. Existing sim-to-real methods often mitigate this gap by simplifying tactile data into coarse low-dimensional features -- sacrificing the richness required for complex manipulation. In this work, we introduce Center-of-Pressure (CoP), an effective tactile representation ground
The paper addresses a core challenge in sim-to-real transfer for dexterous manipulation, a key area of research with significant investment in advanced robotics and AI. This is a critical building block for scaling future robotic applications.
This development can significantly accelerate the deployment of agile robots in unstructured environments, a prerequisite for advanced automation across various industries. More effective sim-to-real transfer directly impacts the pace of robotic innovation.
By more effectively bridging the sim-to-real gap for tactile data, this research allows fine-grained sensing to be fully utilized in robotic control, moving beyond simplified data representations. It enables more robust and precise robotic interactions with the physical world.
- · Robotics companies
- · AI hardware manufacturers
- · Logistics and manufacturing sectors
- · Research institutions in AI/Robotics
Improved robotic control and dexterity in complex manipulation tasks.
Faster development cycles and deployment of robots in areas requiring fine motor skills, such as assembly or advanced surgery.
Enhanced capabilities of general-purpose humanoid robots, making them more adaptable to diverse real-world scenarios.
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