arXiv:2606.04269v1 Announce Type: cross Abstract: Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes. We introduce Instant-Fold, an in-context imitation learning framework for DOM. Given a single human demonstration, our policy infers and executes diverse manipulation modes directly from the demonstration, including variations in spatial execution and ordering, without requiring gradient updates. Our approach first learns deformation-awar
The continuous advancements in AI and imitation learning are enabling more sophisticated robotic manipulation capabilities, especially for complex tasks like deformable object handling.
Improving robot dexterity with deformable objects unlocks new applications in manufacturing, logistics, healthcare, and services, previously limited by rigid-body assumptions.
Robots can now learn and adapt to diverse manipulation modes for deformable objects from minimal demonstrations, reducing programming effort and increasing deployment flexibility.
- · Robotics companies
- · Logistics and e-commerce
- · Healthcare and elder care
- · Food processing
- · Manual labor in repetitive tasks involving deformable objects
- · Companies reliant on rigid-body robotics for certain applications
Increased automation of tasks involving textiles, food, and biological materials.
Reduced operational costs and increased efficiency in industries adopting this advanced manipulation.
Accelerated development of general-purpose robots capable of performing a wider range of human-like tasks in unstructured environments.
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Read at arXiv cs.AI