Overview
Sym2Real is a data-driven framework designed for the data-efficient adaptation of low-level controllers. The framework addresses challenges associated with symbolic regression in real-world control applications, particularly its sensitivity to measurement noise. Sym2Real utilizes a dual-stage learning process to achieve adaptation with a limited amount of real-world data.
Research Context
Symbolic regression offers data-efficient characteristics; however, its application in real-world control has been constrained by its susceptibility to measurement noise. This noise can corrupt equations derived from real-world data, leading to model degradation when symbolic regression is applied directly. The objective behind Sym2Real was to mitigate this limitation and enable robust, data-efficient adaptation in practical control scenarios.
Approach
The Sym2Real framework operates through two primary stages:
- Learning from Low-Fidelity Simulation: Initially, the system learns from low-fidelity simulations. In this environment, the absence of noise in trajectories allows symbolic regression to effectively identify underlying dynamics without degradation.
- Targeted Residual Adaptation: Following the simulation-based learning, a small quantity of real-world data is employed for targeted residual adaptation. This stage is designed to bridge the sim-to-real gap, compensating for discrepancies between the simulated and real environments.
This methodology aims to leverage the data efficiency of symbolic regression while counteracting its sensitivity to real-world noise through a focused adaptive phase.
Findings
- The Sym2Real framework achieved robust control of both a quadrotor and a racecar in real-world conditions.
- This control was accomplished using approximately 10 trajectories for adaptation.
- The framework operated without requiring expert knowledge or specific simulation tuning.
- Experimental validation indicated consistent data-efficient adaptation across six out-of-distribution sim2sim scenarios.
- Successful sim2real transfer was demonstrated across five distinct real-world conditions.
Why This Matters
The ability of Sym2Real to achieve robust control of physical systems with minimal real-world data and without expert tuning addresses a significant challenge in adaptive control. Its proficiency in bridging the sim-to-real gap and handling measurement noise enhances the practicality and applicability of data-driven control methods in real-world environments.