Celebi’s choice: causality-guided skill optimisation for granular manipulation via differentiable simulation

Robotic soil manipulation is essential for automated farming, particularly in excavation and levelling tasks. However, the nonlinear dynamics of granular materials challenge traditional control methods, limiting stability and efficiency. We propose Celebi, a causality-enhanced optimisation method that integrates differentiable physics simulation with adaptive step-size adjustments based on causal inference. To enable gradient-based optimisation, we construct a differentiable simulation environment for granular material interactions. We further define skill parameters with a differentiable mapping to end-effector motions, facilitating efficient trajectory optimisation. By modelling causal effects between task-relevant features extracted from point cloud observations and skill parameters, Celebi selectively adjusts update step sizes to enhance optimisation stability and convergence efficiency. Experiments in both simulated and real-world environments validate Celebi’s effectiveness, demonstrating robust and reliable performance in robotic excavation and levelling tasks.