Deformable Object Manipulation
13 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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VIRDO Dataset
Latest papers with no code
D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation
In this work, we propose D-Cubed, a novel trajectory optimisation method using a latent diffusion model (LDM) trained from a task-agnostic play dataset to solve dexterous deformable object manipulation tasks.
Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud
In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object.
Make a Donut: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools
Remarkably, our model demonstrates robust generalization capabilities to novel and previously unencountered complex tasks without any preliminary demonstrations.
Learning Generalizable Tool-use Skills through Trajectory Generation
We propose to learn a generative model of the tool-use trajectories as a sequence of tool point clouds, which generalizes to different tool shapes.
DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation
An issue arises, however, with the reliance on the specification of a goal shape.
GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy
Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world.
SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation
Deformable object manipulation presents a unique set of challenges in robotic manipulation by exhibiting high degrees of freedom and severe self-occlusion.
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects.
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation
In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states' values for long-term manipulation.
Robotic Fabric Flattening with Wrinkle Direction Detection
Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc.
