Multifidelity Sim-to-Real Pipeline

• The topic introduces multifidelity sim-to-real pipelines, which integrate models at different fidelities to reduce discrepancies between simulated and real environments.
• The methodology combines hierarchical, parallel, and domain randomization techniques, employing convex combinations of simulators to optimize policy learning.
• Empirical results in robotics show substantial improvements in performance and robustness, while addressing challenges such as scalability and system complexity.

A multifidelity sim-to-real pipeline refers to any system for robotic learning, planning, or inference that systematically incorporates models, data, or simulators at several distinct levels of physical, geometric, or statistical fidelity to produce robust real-world performance. These pipelines are characterized by hierarchical or parallel integration of components ranging from low-fidelity (fast, approximate, abstracted) to high-fidelity (realistic, slow, resource-intensive) models, with the explicit goal of minimizing computational cost while maintaining, or even improving, sim-to-real transfer success.

1. Mathematical Formulation and Theoretical Foundations

The core theoretical insight behind multifidelity sim-to-real pipelines is that leveraging a convex combination of multiple simulators or models with varying inductive biases can yield a tighter upper bound on the sim-to-real gap than relying on any single simulator (Lei et al., 2 Oct 2025). Suppose the real environment is given by $$M^* = (\mathcal{S}_0, \mathcal{A}, T_0, R_0, \gamma)$$, and a family of simulators $$\{M_i = (\mathcal{S}_i, \mathcal{A}, T_i, R_i, \gamma)\}_{i=1}^N$$ is available, each mapped to the real state space via projections and measurable sections $f_i, g_i$ so that the "lifted" transition kernel is

$$\tilde{T}_i(\cdot|s,a) := (g_i)_\# T_i(\cdot|f_i(s), a).$$

The composite transition kernel over mixture weights $w \in \Delta^N$ (the probability simplex) is

$$T_Q(\cdot|s,a) = \sum_{i=1}^N w_i \tilde{T}_i(\cdot|s,a).$$

For any policy $\pi$, the discounted return is

$$\eta_{T, R}(\pi) = \mathbb{E}\left[ \sum_{t \geq 0} \gamma^t R(s_t, a_t) \right].$$

The sim-to-real gap for mixture $Q$ is

$$G_{S2R}(Q; \Pi) := \sup_{\pi \in \Pi} |\eta_{T_0, R_0}(\pi) - \eta_{T_Q, R_0}(\pi)|.$$

The principal theoretical result is that

$G_{S2R}(Q; \Pi) \leq \min_i G_{S2R}(P_i; \Pi),$

which means that training over a convex hull of simulator dynamics leads to a worst-case gap no worse than the most accurate individual model (Lei et al., 2 Oct 2025). The geometric interpretation, using the 1-Wasserstein metric, supports the efficacy of multi-simulator domain randomization over single-model training.

2. Hierarchical and Parallel Model Integration

Multifidelity sim-to-real pipelines are often hierarchical or parallel in structure, combining models of different fidelity across spatial or temporal segments, modules, or subtasks. Classic hierarchical architectures include:

• Local/Global Planning: High-fidelity, jerk-controlled local planners for UAVs near the vehicle; lower-fidelity velocity-controlled or pure geometric planners for global path planning (Tordesillas et al., 2018).
• Surrogate Modeling: Training neural surrogates on abundant low-fidelity simulation data, refining outputs or output layers using limited high-fidelity data with transfer learning and fine-tuning (Jiang et al., 2022).
• Replay Buffers and Sampling: Maintaining parallel buffers for high-throughput, low-fidelity samples (simulated) and low-throughput, high-fidelity samples (real), with different priorities for sampling and optimization (Shashua et al., 2021).
• Multi-Simulator Randomization: Simultaneous (parallelized) training in different physics engines or approximate models, exposing the learning policy to a composite of simulator inductive biases, as in PolySim (Lei et al., 2 Oct 2025).

Parallelisms between model levels (for example, training across IsaacGym, IsaacSim, and Genesis) produce a convex set of experienced dynamics, reducing overfitting to a single simulator’s artifacts and capturing a broader envelope of plausible real-world behaviors.

3. Domain Randomization and Simulation Gap Management

Domain randomization and multi-simulator domain randomization are complementary strategies within the multifidelity paradigm:

• Parameter and Observation Randomization: Random perturbations of dynamics parameters, sensor noise, lighting, object positions, camera calibration, and actuation delay are injected in simulation to bridge the reality gap (Williams et al., 13 May 2025, Wang et al., 10 Apr 2025). This approach teaches agents to become invariant to nuisance variability that differentiates simulation from reality.
• Multi-Simulator Dynamics Randomization: Unlike internal parameter shuffling in a single engine, PolySim achieves randomization by synchronously launching and harmonizing multiple heterogeneous simulators, each embodying distinct physics implementation assumptions, discretization errors, and contact models (Lei et al., 2 Oct 2025). This exposes policies directly to the class of simulator-model discrepancies that cause real sim-to-real failures, aligning the statistical support of the training distribution more tightly with the real world.

Additionally, some pipelines employ curriculum learning (gradually increasing difficulty) and system identification (calibrating simulation parameters by matching measured real-world system responses) to further align simulated and real domains (Wang et al., 10 Apr 2025, Silveira et al., 21 Feb 2025).

4. Learning Algorithms and Control Policies

Multifidelity sim-to-real pipelines can encompass a variety of learning strategies:

• Reinforcement Learning with Transfer: Hierarchical deep RL in which initial policy knowledge is acquired in low-fidelity, computationally cheap simulators before being transferred and fine-tuned in high-fidelity scenarios (e.g., using PPO for airfoil design with transfer control using monitored reward variance) (Bhola et al., 2022).
• Policy Gradient with Control Variates: Mixing low-fidelity and high-fidelity Monte Carlo policy gradient estimators to construct an unbiased, reduced-variance gradient; notably in Multi-Fidelity Policy Gradients (MFPG), control variate coefficients are adapted online based on sample covariance (Liu et al., 7 Mar 2025).
• Imitation/Behavioral Cloning: Training exclusively in simulation using high-fidelity visual or proprioceptive renderings, collecting expert data using privileged information or precomputed trajectories, and deploying with little to no real-world adaptation (e.g., Re³Sim’s pipeline for robotic manipulation, FalconGym’s photorealistic quadrotor control) (Han et al., 12 Feb 2025, Miao et al., 4 Mar 2025).
• Adaptive and Hierarchical Controllers: Inclusion of fast, low-level model-based controllers (adaptive or PID) layered below high-level learned policies, improving stability and tracking in under-actuated or high-speed systems (Wang et al., 10 Apr 2025, Tordesillas et al., 2018).

In all strategies, real-world deployment is often proceeded by verification in “software-in-the-loop” or “hardware-in-the-loop” environments to catch failures before physical execution (Neary et al., 2023).

5. Empirical Results and Real-World Validation

Experimental studies across diverse domains establish the efficacy and limitations of multifidelity sim-to-real pipelines:

Study/Domain	Pipeline Structure	Real-World Result Summary
PolySim (Lei et al., 2 Oct 2025)	Parallel multi-simulator RL for humanoid WBC	52.8% improvement sim-to-sim; zero-shot transfer to real Unitree G1
Agile UAV Planning (Tordesillas et al., 2018)	Jerk (local) + velocity (mid) + geometric (global)	19–47% shorter paths, 5–40 ms replanning, agile flight onboard
Manipulation Surrogate (Jiang et al., 2022)	LF-to-HF U-Net with transfer and fine-tune	~90% reduction in HF data, near-HF accuracy
RL for VPP MAV (Wang et al., 10 Apr 2025)	System ID + domain randomization + hierarchical control	Zero-shot flips, wall-backtrack maneuvers
Multi-Fidelity Policy Gradients (Liu et al., 7 Mar 2025)	REINFORCE & PPO with CVs from sim+real	Up to 3.9× higher reward vs. HF-only baselines
Fruit Harvesting (Williams et al., 13 May 2025)	Domain randomized sim + DRM RL + impedance ctrl	Lab robot zero-shot picks in cluttered scenes

Motion tracking error, success rates, energy penalties, robustness to perception and actuation noise, and resource efficiency are consistently reported. Pipelines with parallel multi-simulator exposure (PolySim) systematically outperform staged or single-fidelity procedures, and real-to-sim-to-real approaches with dynamic digital twins—where the real robot follows the continuously synchronized simulation—show near parity between simulated and real-world evaluation (Abou-Chakra et al., 4 Apr 2025).

6. Implementation, Limitations, and Future Directions

Implementation requires significant engineering—harmonizing simulator APIs, state conventions, control rates, reward shaping, and managing data across parallel environments. Limitations identified include:

• Residual Gap from Real-World Complexity: Not all physical effects (e.g., sensor latency, friction, actuator stiction) are captured, and shared abstractions across simulators may erase important high-order details (Lei et al., 2 Oct 2025, Noorani et al., 14 Mar 2025).
• Scalability and Onboarding New Tasks: High-dimensional control spaces, complex objects (deformable, articulated), and unstructured environments challenge current pipelines, especially as the number of simulators increases (“curse of heterogeneity”) (Han et al., 12 Feb 2025).
• Complexity of Task Decomposition: Decomposing tasks for compositional RL requires formal definition of entry/exit interfaces and robust high-level model synthesis; failures in one subtask can propagate unless the pipeline allows for targeted refinement (Neary et al., 2023).
• Resource Constraints: High-fidelity simulators are expensive; low-fidelity models may need careful selection to ensure mutual information or parameter coverage with target domains (Bhola et al., 2022, Krouglova et al., 12 Feb 2025).

Areas for further research suggested include asynchronous training across heterogeneous simulators, exploration of task similarity metrics (information-theoretic), extensions to uncertain and non-stationary domains, and integration with foundation models or advanced domain adaptation techniques (Dan et al., 11 May 2025, Krouglova et al., 12 Feb 2025, Noorani et al., 14 Mar 2025).

7. Broader Implications and Applications

Multifidelity sim-to-real pipelines are increasingly foundational in robotic control, perception, simulation-based inference, and scientific computing. Their benefits—rapid policy learning with reduced real-world data, improved robustness to environment shift, and scalable evaluation—are critical as robots and agents are deployed in dynamic, unstructured, and previously unseen contexts. The integration of hierarchical and parallel models, carefully designed transfer and adaptation mechanisms, and real-world feedback via online synchronization or calibration, represent the current state of best practice in closing the sim-to-real gap across domains.