Simulating a Tactile Sensor in MuJoCo

Notes on building a MuJoCo approximation of a 3-channel tactile sensor.

One part of my UCL visit is about replicating a 3-channel tactile sensor in MuJoCo for data collection and training. The goal is not to build a perfect copy of the real sensor. The goal is to create a simulation that produces useful tactile structure for learning.

What needs to be simulated

A useful tactile simulation needs at least:

• Contact location across the sensor surface.
• Normal force at contact points.
• Shear forces in two lateral directions.
• Temporal changes as objects move, slip, or press into the surface.

The difficult part is matching the signal statistics closely enough that models trained on simulation learn transferable structure.

The modeling tradeoff

A high-fidelity tactile simulation can become expensive quickly. But a very simple proxy may miss the contact cues needed for downstream tasks. The practical question is where the approximation should live.

For representation learning, it may be enough for the simulation to capture the relationships between contact, shape, and force dynamics, even if every taxel is not physically perfect.

Data collection loop

The imagined loop is:

1. Sample objects, poses, and interactions in simulation.
2. Record normal and shear force maps over time.
3. Train MAE/JEPA-style models on the generated tactile streams.
4. Test whether the learned representation transfers to real tactile data.

Open questions

• Which contact model best matches the real sensor behavior?
• How much domain randomization is useful?
• Should the network see raw forces or preprocessed tactile maps?
• What downstream task best measures whether the simulation is good enough?