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

Wei, Minglun, Yang, Xintong ORCID: https://orcid.org/0000-0002-7612-614X, Yan, Junyu, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2025. Celebi’s choice: causality-guided skill optimisation for granular manipulation via differentiable simulation. Presented at: IROS 2025, Hangzhou, China, 19-25 October 2025. Proceedings of the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025). IEEE,

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Status:	In Press
Schools:	Schools > Engineering Schools > Computer Science & Informatics
Publisher:	IEEE
Date of First Compliant Deposit:	29 July 2025
Date of Acceptance:	16 June 2025
Last Modified:	29 Aug 2025 01:45
URI:	https://orca.cardiff.ac.uk/id/eprint/180126

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