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A Software Ecosystem for Research in Reinforcement Learning-based Receding Horizon Control.

Main framework packages:

• LRHControl: Learning-based Receding Horizon Control exploiting Reinforcement Learning.
• CoClusterBridge: tool for bridging a CPU-based control cluster with parallel simulation environments.
• OmniRoboGym: wrapper on top of Omniverse Isaac Sim tailored to legged robotics control with learning techniques.
• SharsorIPCpp: shared matrices built on top of POSIX-IPC and Eigen, shipped with python bindings and NumPy support.
• RHCViz: Ros-powered minimal tool to visualize Receding Horizon Control solutions on RViz in realtime.

Other relevant dependencies:

• IsaacSim: GPU-accelerated robotics simulator for AI.
• horizon: a trajectory optimization tool, tailored to robotics and based on Casadi.

Container deployment utils: ibrido-containers.