What is the predictive coding interpretation of Sim2Real?

It views successful transfer as the ability of a policy's internal model to accurately predict the sensory consequences of actions in a new physical environment, treating the simulation as a prior distribution.

How can I diagnose why my robot is failing in the real world?

The skill includes a diagnostic framework using Perception/Action Divergence (PAD) to determine if failures stem from observation mismatch, dynamics mismatch, or overconfident predictions.

Which robotic frameworks is this skill designed for?

This skill focuses on DeepMind's MuJoCo Playground and K-Scale's JAX-based ksim framework for humanoid training.

How does this skill help with zero-shot transfer?

It provides implementation patterns for noise injection, latency modeling, and entropy regularization to prevent policies from overfitting to simulation-specific physics.

Sim2Real Predictive Coding

Name: Sim2Real Predictive Coding
Author: plurigrid

byplurigrid

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データサイエンスとML

Bridges the gap between robotic simulations and real-world deployment using active inference and predictive coding frameworks.

This skill provides a theoretical and practical framework for Sim2Real transfer by interpreting the process through the lens of predictive coding and active inference. It integrates advanced concepts from the MuJoCo Playground and K-Scale ksim frameworks to model domain randomization as uncertainty and leverage Variational Free Energy (VFE) for diagnosing transfer failures. By aligning simulation fidelity with predictive accuracy, the skill enables developers to build robust robotic policies that effectively handle observation noise, action latency, and model mismatches during zero-shot transfer.

主な機能

01Integrates seamlessly with MuJoCo Playground and K-Scale ksim humanoid training

02Interprets Sim2Real transfer via active inference and predictive coding principles

03Provides diagnostic tools for transfer failure using Perception/Action Divergence (PAD)

04Uses domain randomization as a formal method for uncertainty modeling

058 GitHub stars

06Implements observation noise injection and action latency modeling for robust training

ユースケース

01Optimizing domain randomization parameters to ensure real-world physics fall within sim support

02Debugging policy failures on physical hardware by analyzing prediction-action divergence

03Training humanoid robotic policies in simulation for successful zero-shot real-world deployment

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi sim2real-predictive-coding

For use in Claude.ai and ChatGPT

Download Skill