LITHE: Bridging Best-Effort Python and Real-Time C++ for Hot-Swapping Robotic Control Laws on Commodity Linux

TL;DR

LITHE enables dynamic, real-time evolution of robotic control laws by bridging Python and C++ on commodity hardware, allowing safe hot-swapping without interrupting high-frequency control loops.

Contribution

LITHE introduces a lightweight architecture combining CPU isolation, lock-free IPC, and multi-threaded linking to dynamically update real-time controllers from Python without disruption.

Findings

Achieves WCET < 100 μs and jitter < 4 μs under load.

Enables Python-based AI to modify real-time control logic on-the-fly.

Demonstrates online system identification with LLM supervision.

Abstract

Modern robotic systems rely on hierarchical control, where a high-level "Brain" (Python) directs a lower-level "Spine" (C++ real-time controller). Despite its necessity, this hierarchy makes it difficult for the Brain to completely rewrite the Spine's immutable control logic, consequently inhibiting fundamental adaptation for different tasks and environments. Conventional approaches require complex middleware, proprietary hardware, or sacrifice real-time performance. We present LITHE (Linux Isolated Threading for Hierarchical Execution), a lightweight software architecture that collapses the robot control hierarchy onto a commodity single-board computer (Raspberry Pi 4B with pi3hat), while maintaining safe frequency decoupling between the Brain and Spine. LITHE integrates strict CPU isolation (isolcpus), lock-free inter-process communication (IPC), and pipelined execution to meet high-frequency deadlines with minimal jitter. By adding multi-threaded dynamic linking, LITHE enables a Python-based Brain to dynamically evolve the logic of a 1kHz C++ Spine without interruption. We validate "functional real-time" system performance with worst-case execution time (WCET) < 100 $\mu$s and maximum release jitter (MRJ) < 4 $\mu$s under heavy load. We demonstrate a novel application where a large language model (LLM) supervisor performs online system identification to evolve a real-time controller on-the-fly, without interrupting the 1 kHz control loop. In essence, LITHE eliminates the "immutable compiled code" bottleneck for best-effort Brains to synthesize and inject completely new control laws into the real-time Spine. This bridges a critical gap between high-level AI and low-level real-time control to unlock continuous real-time evolution of embodied intelligence in safe, human-in-the-loop systems.