Bridging the Sim-to-Real Gap with multipanda_ros2: A Real-Time ROS2 Framework for Multimanual Systems

Model-based development accelerates prototyping, enables earlier experimentation, and ensures rigorous validation of system design intents. In multi-agent systems with complex asynchronous interactions and concurrency, formal verification, particularly model-checking, offers an automated means of confirming that desired properties hold. Timed Rebeca, an actor-based modelling language supporting reactive, concurrent, and timed behaviors, together with its model-checking tool, provides a powerful framework for this purpose. By leveraging these capabilities, Timed Rebeca can intuitively capture ROS2 node graphs, recurring physical signals, motion primitives, and other time-convertible behaviors. Nevertheless, modelling and verifying multi-robot systems entail significant challenges: abstracting intricate information, bridging the gap between discrete models and continuous system dynamics, and managing large state spaces while preserving fidelity. To address these challenges, we propose discretization strategies tailored to various data types and identify thresholds of abstraction that balance accuracy and tractability. We further introduce optimization techniques to accelerate verification. Our work demonstrates how to systematically design and verify multi-robot systems through Timed Rebeca, efficiently transform continuous dynamics into discrete models for model-checking, and maintain a practical, bidirectional flow between the abstract model and the ROS2 implementation. The accompanying Rebeca and ROS2 codebases, made openly available, serve as a foundational reference for researchers and developers aiming to model and verify advanced autonomous robotic systems.

This paper introduces a novel modular architecture for ROS2 that decouples the logic required to acquire, validate, and interpolate references from the control laws that track them. The design includes a dedicated component, named Reference Generator, that receives references, in the form of either single points or trajectories, from external nodes (e.g., planners), and writes single-point references at the controller's sampling period via the existing ros2_control chaining mechanism to downstream controllers. This separation removes duplicated reference-handling code from controllers and improves reusability across robot platforms. We implement two reference generators: one for handling joint-space references and one for Cartesian references, along with a set of new controllers (PD with gravity compensation, Cartesian pose, and admittance controllers) and validate the approach on simulated and real Universal Robots and Franka Emika manipulators. Results show that (i) references are tracked reliably in all tested scenarios, (ii) reference generators reduce duplicated reference-handling code across chained controllers to favor the construction and reuse of complex controller pipelines, and (iii) controller implementations remain focused only on control laws.

The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world systems. We propose a flexible Real-to-Sim-to-Real (RSR) framework whose central contribution is an information-theoretic cost function that explicitly accounts for sim-to-real discrepancies. This cost balances two objectives, completing the task and steering the policy to collect real-world samples that are maximally informative for improving transfer. It can be integrated seamlessly into existing reinforcement learning algorithms (e.g., PPO, SAC) and ensures a balanced exploration of critical regions in the real domain. The framework treats differentiable simulation as optional: when a differentiable simulator is available, the collected informative data can also be used to tune simulator parameters. We implement the framework with the MuJoCo MJX platform and demonstrate its generality by evaluating on both manipulation tasks with a 6-DOF robotic arm and locomotion tasks on a legged robot. Empirical results show that our RSR loop yields more efficient data acquisition and substantially improves task performance in real-world that achieves a smoother sim-to-real transfer.