Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation

2026-05-27 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

A new tactile representation, Center-of-Pressure (CoP), has been introduced to enhance sim-to-real reinforcement learning for dexterous manipulation. This method addresses the simulation-reality gap that typically hinders the effective use of information-dense touch data by simplifying it. CoP is grounded in physical principles, allowing it to preserve rich contact information while remaining robust for transfer from simulation to real-world robots. To support CoP, the researchers propose a sensor calibration scheme utilizing differentiable dynamics, which estimates taxel orientations without needing ground-truth force measurements. Evaluated on challenging contact-rich tasks like peg-in-hole insertion and ball balancing, CoP-conditioned policies demonstrated zero-shot sim-to-real transfer on a multi-fingered hand. These policies consistently outperformed baselines using coarse binary-contact and raw-taxel data, and analysis showed they implicitly encode physical properties such as object mass.

Key takeaway

For robotics engineers developing dexterous manipulation systems, adopting physics-grounded tactile representations like Center-of-Pressure (CoP) is crucial for bridging the sim-to-real gap. You can achieve zero-shot transfer for contact-rich tasks, avoiding extensive real-world data collection. Consider integrating differentiable dynamics for sensor calibration, as this approach enables robust tactile sensing without requiring ground-truth force measurements, significantly streamlining your development workflow and improving policy performance on complex tasks.

Key insights

Center-of-Pressure (CoP) enables robust sim-to-real dexterous manipulation by preserving dense, physics-grounded tactile information.

Principles

• Physics-grounded representations improve sim-to-real transfer.
• Differentiable dynamics can calibrate sensors without ground truth.
• Rich tactile data enhances complex manipulation.

Method

A sensor calibration scheme uses differentiable dynamics to estimate taxel orientations, supporting the Center-of-Pressure (CoP) tactile representation for sim-to-real transfer in contact-rich tasks.

In practice

• Apply CoP for zero-shot sim-to-real transfer.
• Use differentiable dynamics for sensor calibration.
• Improve policies for peg-in-hole and ball balancing.

Topics

• Sim-to-Real Transfer
• Dexterous Manipulation
• Tactile Sensing
• Center-of-Pressure
• Reinforcement Learning
• Differentiable Dynamics

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.