What specific software stacks are compatible with this skill?

It is optimized for use with K-Scale Labs tools (ksim, kos, kinfer), DeepMind's MuJoCo Playground, and JAX-based physics engines.

How does this skill handle Sim2Real transitions?

It treats Sim2Real as an inference problem about future observations, utilizing KL divergence minimization to bridge the gap between simulation data and real-world sensorimotor loops.

What is the primary focus of the active-inference-robotics skill?

It focuses on synthesizing Patrick Kenny's active inference framework with modern JAX-based robotics stacks to create predictive locomotion policies based on free energy minimization.

Can this skill be used with standard Reinforcement Learning?

Yes, it provides a mapping that translates active inference theory into standard RL implementations, such as using PPO gradient steps as approximations for mean field updates.

Active Inference Robotics

Name: Active Inference Robotics
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Synthesizes Patrick Kenny's active inference framework with K-Scale's JAX/MuJoCo robotics stack for advanced predictive coding in robot locomotion.

소개

This skill provides a specialized bridge between theoretical active inference and practical robotics engineering. It integrates Patrick Kenny’s discrete active inference framework with K-Scale’s JAX-based MuJoCo stack, allowing developers to implement predictive coding for robot control. By mapping concepts like Variational Free Energy and Mean Field updates to Reinforcement Learning patterns such as PPO gradient steps and entropy regularization, this skill enables the creation of sophisticated locomotion policies. It is particularly useful for projects involving Sim2Real transitions, hierarchical control loops, and bio-mimetic state estimation.

주요 기능

Supports JAX-accelerated MuJoCo simulation for high-performance robot training
Maps Active Inference concepts (VFE/EFE) directly to RL implementations and PPO losses
Provides implementation patterns for reflexive control and deliberative planning
Synthesizes Patrick Kenny’s PAD (Perception/Action Divergence) with K-Scale's ksim
Enables hierarchical composition from high-level goal selection to motor primitives
2 GitHub stars

사용 사례

Implementing Sim2Real frameworks using inference about future observations
Designing closed-loop control systems that minimize surprise in sensory feedback
Developing bio-mimetic locomotion policies for humanoid and quadruped robots

소개

주요 기능

Supports JAX-accelerated MuJoCo simulation for high-performance robot training
Maps Active Inference concepts (VFE/EFE) directly to RL implementations and PPO losses
Provides implementation patterns for reflexive control and deliberative planning
Synthesizes Patrick Kenny’s PAD (Perception/Action Divergence) with K-Scale's ksim
Enables hierarchical composition from high-level goal selection to motor primitives
2 GitHub stars

사용 사례

Implementing Sim2Real frameworks using inference about future observations
Designing closed-loop control systems that minimize surprise in sensory feedback
Developing bio-mimetic locomotion policies for humanoid and quadruped robots