CLASH: Collision Learning via Augmented Sim-to-real Hybridization to Bridge the Reality Gap

TL;DR

Collision Learning via Augmented Sim-to-real Hybridization (CLASH) is introduced, a data-efficient framework that creates a high-fidelity hybrid simulator by learning a surrogate collision model from a minimal set of real-world data.

Abstract

The sim-to-real gap, particularly in the inaccurate modeling of contact-rich dynamics like collisions, remains a primary obstacle to deploying robot policies trained in simulation. Conventional physics engines often trade accuracy for computational speed, leading to discrepancies that prevent direct policy transfer. To address this, we introduce Collision Learning via Augmented Sim-to-real Hybridization (CLASH), a data-efficient framework that creates a high-fidelity hybrid simulator by learning a surrogate collision model from a minimal set of real-world data. In CLASH, a base model is first distilled from an imperfect simulator (MuJoCo) to capture general physical priors; this model is then fine-tuned with a remarkably small number of real-world interactions (as few as 10 samples) to correct for the simulator's inherent inaccuracies. The resulting hybrid simulator not only achieves higher predictive accuracy but also reduces collision computation time by nearly 50\%. We demonstrate that policies obtained with our hybrid simulator transfer more robustly to the real world, doubling the success rate in sequential pushing tasks with reinforecement learning and significantly increase the task performance with model-based control.

Figures (6)

• Figure 1: Overview of our CLASH framework and learning downstream tasks with the hybrid simulator.
• Figure 2: Our framework Collision Learning via Augmented Sim-to-real Hybridization (CLASH): First collect collision dataset from physics simulator and pre-train a base model to learn the collision dynamics, then adopt the model to identify the system parameters for real-world collision. Subsequently, fine-tune with real-world dataset to obtain a collision model. Finally, integrate the collision model with the physics simulator to build a hybrid simulator that is close to reality.
• Figure 3: (a) The proposed hybrid simulator is applied to two downstream tasks: model-based optimization and reinforcement learning. (b) System setup, where a Franka arm manipulates an impact block to strike the semi-cylinder block.
• Figure 4: Collision model accuracy ($\alpha=0.1$) in different settings. Our fine-tuned strategy effectively avoids overfitting on scarce real-world data and achieves higher accuracy than MuJoCo, whose performance is limited by unmodeled effects.
• Figure 5: Linear and angular velocities of the square block in simulation and real world. MuJoCo captures the linear velocity decay reasonably well, but fails to reproduce the angular velocity decay accurately. Raw: raw measurements from the real world. Smoothed: filtered real-world data. Simulation: MuJoCo-simulated data.