Robotic Paper Wrapping by Learning Force Control
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Abstract (arXiv)
Robotic packaging using wrapping paper poses significant challenges due to the material's complex deformation properties. The packaging process itself involves multiple steps, primarily categorized as folding the paper or creating creases. Small deviations in the robot's arm trajectory or force vector can lead to tearing or wrinkling of the paper, exacerbated by the variability in material properties. This study introduces a novel framework that combines imitation learning and reinforcement learning to enable a robot to perform each step of the packaging process efficiently. The framework allows the robot to follow approximate trajectories of the tool-center point (TCP) based on human demonstrations while optimizing force control parameters to prevent tearing or wrinkling, even with variable wrapping paper materials. The proposed method was validated through ablation studies, which demonstrated successful task completion with a significant reduction in tear and wrinkle rates. Furthermore, the force control strategy proved to be adaptable across different wrapping paper materials and robust against variations in the size of the target object.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 19 Mar 2025.
Builds on
17 of 30 references.
References · 17
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5 more
- 2019Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisorcited by 44
- 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 37
- 2019Dynamic Cloth Manipulation with Deep Reinforcement Learningcited by 26
- 2023Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearrangingcited by 6
- 2023Learning Neural Force Manifolds for Sim2Real Robotic Symmetrical Paper Foldingcited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024ScissorBot: Learning Generalizable Scissor Skill for Paper Cutting via Simulation, Imitation, and Sim2Real
- 2024Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation
- 2026Online Material Estimation for Conditioned Diffusion Policy in Shaping Deformable Linear Objects
- 2025FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation
- 2024ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation
- 2025Robotic Compliant Object Prying Using Diffusion Policy Guided by Vision and Force Observations
- 2022Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
- 2025Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representations