Does this skill support JAX?

Yes, the provided implementations utilize JAX and jax.numpy for high-performance computations, allowing for efficient gradient-based optimization of entropy distributions.

What distribution alignment metrics are included?

The skill provides implementations for Wasserstein distance (optimal transport), Maximum Mean Discrepancy (MMD), and KL divergence to measure and minimize the gap between sim and real data.

What is the 'reality gap' in robotics?

The reality gap refers to the discrepancy between a simulated environment and the physical world, which often causes AI models to fail when moved from software to real hardware.

How does entropy help in sim2real transfer?

High entropy in simulation ensures the policy experiences a wide range of conditions, making it more robust and preventing it from overfitting to specific, imperfect simulated physics.

Entropy Sim2Real Bridge

Name: Entropy Sim2Real Bridge
Author: plurigrid

byplurigrid

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Data Science & ML

Bridges the gap between robotic simulations and real-world deployment using maximum entropy reinforcement learning and information-theoretic alignment.

About

The Entropy Sim2Real skill provides a specialized framework for overcoming the 'reality gap' in robotics and reinforcement learning. By leveraging the principle of maximum entropy, it enables developers to train policies in simulation that remain robust when transferred to physical hardware. The skill implements entropy-maximizing domain randomization to ensure broad parameter coverage, policy regularization to prevent overconfidence, and information-theoretic measures like Wasserstein distance and MMD to align simulated distributions with real-world sensor data.

Key Features

2 GitHub stars
Information-theoretic bridging using Wasserstein and MMD metrics
Maximum Entropy Reinforcement Learning (MaxEnt RL) with PPO integration
Entropy-maximizing domain randomization for comprehensive parameter coverage
JAX-optimized implementations for high-performance distribution matching
Adaptive randomization routines based on real-world sample moments

Use Cases

Transferring complex manipulation tasks from physics engines to physical robotic arms
Calibrating simulation parameters to match real-world sensor noise and mechanical friction
Deploying legged robots trained in simulation to unpredictable outdoor terrains

About

Key Features

2 GitHub stars
Information-theoretic bridging using Wasserstein and MMD metrics
Maximum Entropy Reinforcement Learning (MaxEnt RL) with PPO integration
Entropy-maximizing domain randomization for comprehensive parameter coverage
JAX-optimized implementations for high-performance distribution matching
Adaptive randomization routines based on real-world sample moments

Use Cases

Transferring complex manipulation tasks from physics engines to physical robotic arms
Calibrating simulation parameters to match real-world sensor noise and mechanical friction
Deploying legged robots trained in simulation to unpredictable outdoor terrains