Online Material Estimation for Conditioned Diffusion Policy in Shaping Deformable Linear Objects
On the map: Diffusion models → Diffusion and flow policies for robotics
Abstract (arXiv)
Shape control of deformable linear objects (DLOs) is challenging for imitation learning because deformation behavior varies with material properties such as stiffness and elasticity, so a single policy must generate different action sequences for different objects even when the goal shape is identical. We propose a diffusion policy conditioned on material labels that are estimated online during manipulation. A recurrent estimation network predicts the material label of the grasped object from the time series of multi-view images and robot joint states, and the predicted label conditions the diffusion policy at every inference step. We collected 480 real-robot demonstrations covering four DLO materials and three groove-placement tasks, and compared per-material specialist policies, a task-conditioned policy without material labels, a policy conditioned on ground-truth material labels, and the proposed policy. Conditioning on ground-truth material labels improved the average success rate from 45.8% to 60.0% over the task-only policy, and the proposed policy reached 60.8% without any prior material information, matching the policy given ground-truth labels. A post-hoc analysis shows that the estimator extracts material-related information from the manipulation observations and that the diffusion policy responds to the resulting conditioning signal, while the one pronounced failure case is associated with persistent confusion between two similar materials.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 10 of its 15 references that are in the map. First posted on arXiv 11 Sep 2026.
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- 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
- 2016Interactive Perception: Leveraging Action in Perception and Perception in Actioncited by 78
- 2019DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactionscited by 63
- 2023Multi-Stage Cable Routing through Hierarchical Imitation Learningcited by 60
- 2021SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulationcited by 24
- 2023GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policycited by 3
- 2025RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environmentscited by 3
- 2024Optimal Cosserat-based deformation control for robotic manipulation of linear objectscited by 2
- 2020Adaptive Shape Servoing of Elastic Rods using Parameterized Regression Features and Auto-Tuning Motion Controlscited by 1
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- 2024SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy
- 2024Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects
- 2026Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
- 2025Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy
- 2018Learning-based Feedback Controller for Deformable Object Manipulation
- 2022Learning Deformable Object Manipulation from Expert Demonstrations
- 2026Self-Curriculum Model-based Reinforcement Learning for Shape Control of Deformable Linear Objects