# Deformable Linear Object Manipulations with Differentiable Physics

Anonymous authors
Submission Number: 9432

## DLO-Lab supports a wide range of material properties and
enables versatile manipulations with Deformable Linear Objects (DLOs).

## Abstract

We address the challenge of enabling robots to manipulate deformable linear objects (DLOs), such as wires, ropes, and rubber bands. Prior work in this domain has primarily focused on narrow, task-specific problems, often relying on real-world demonstrations or handcrafted heuristics. Such approaches, however, do not scale to the diverse range of materials and tasks encountered in practice, where collecting sufficiently varied real-world data is impractical. Moreover, existing simulation environments provide limited support for the broad spectrum of material behaviors required for generalizable DLO manipulation.

To overcome these limitations, we introduce a differentiable physics simulator specifically designed for versatile DLO manipulation. Our simulator models a wide range of material properties—including extensibility, inextensibility, elasticity, bending plasticity, and interactions with both rigid and deformable objects—thereby establishing a robust foundation for learning and evaluating manipulation skills.

Building on this simulator, we propose a benchmark suite of representative DLO manipulation tasks that highlight their unique challenges. We further evaluate multiple policy learning algorithms on these tasks. The results show that reinforcement learning can learn closed-loop policies but requires prohibitively large amounts of data. In contrast, trajectory optimization is more efficient: gradient-based methods achieve the best sample efficiency when gradients are available, while sampling-based approaches are broadly applicable but less efficient.

## Benchmark Tasks

We show the manipulation tasks in our benchmark, each with an initial state and the desired goal state.

## Task Demos

### PPO

### CMA-ES

## Robustness Evaluation

We evaluate the robustness of the learned PPO policy for the Separation task under different initial configurations of the DLO.

## Sim-to-Real Demo