# Differentiable Particles for General-Purpose Deformable Object Manipulation

arXiv: https://arxiv.org/abs/2405.01044 · HTML: https://mlsanity.com/paper/2405.01044/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2024.

## Built on by (most cited first, 11 of 11)

- [Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination](https://mlsanity.com/paper/2412.14957/) (2024)
- [DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment](https://mlsanity.com/paper/2410.11584/) (2024)
- [General-purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2408.08160/) (2024)
- [PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided LLM](https://mlsanity.com/paper/2505.16456/) (2025)
- [Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation](https://mlsanity.com/paper/2606.14188/) (2026)
- [Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations](https://mlsanity.com/paper/2505.00500/) (2025)
- [PinchBot: Long-Horizon Deformable Manipulation with Guided Diffusion Policy](https://mlsanity.com/paper/2507.17846/) (2025)
- [CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2507.19983/) (2025)
- [Planning-Query-Guided Model Generation for Model-Based Deformable Object Manipulation](https://mlsanity.com/paper/2508.19199/) (2025)
- [Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control](https://mlsanity.com/paper/2511.03181/) (2025)
- [MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy](https://mlsanity.com/paper/2605.22597/) (2026)

## Abstract (arXiv)

Deformable object manipulation is a long-standing challenge in robotics. While existing approaches often focus narrowly on a specific type of object, we seek a general-purpose algorithm, capable of manipulating many different types of objects: beans, rope, cloth, liquid, . . . . One key difficulty is a suitable representation, rich enough to capture object shape, dynamics for manipulation and yet simple enough to be acquired effectively from sensor data. Specifically, we propose Differentiable Particles (DiPac), a new algorithm for deformable object manipulation. DiPac represents a deformable object as a set of particles and uses a differentiable particle dynamics simulator to reason about robot manipulation. To find the best manipulation action, DiPac combines learning, planning, and trajectory optimization through differentiable trajectory tree optimization. Differentiable dynamics provides significant benefits and enable DiPac to (i) estimate the dynamics parameters efficiently, thereby narrowing the sim-to-real gap, and (ii) choose the best action by backpropagating the gradient along sampled trajectories. Both simulation and real-robot experiments show promising results. DiPac handles a variety of object types. By combining planning and learning, DiPac outperforms both pure model-based planning methods and pure data-driven learning methods. In addition, DiPac is robust and adapts to changes in dynamics, thereby enabling the transfer of an expert policy from one object to another with different physical properties, e.g., from a rigid rod to a deformable rope.