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Found 5 papers, 0 papers with code
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery
This paper introduces a sample-efficient method that learns a robust reward function from a limited amount of ranked suboptimal demonstrations consisting of partial-view point cloud observations.
DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation
An issue arises, however, with the reliance on the specification of a goal shape.
DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects
Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects.
Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds
If robots could reliably manipulate the shape of 3D deformable objects, they could find applications in fields ranging from home care to warehouse fulfillment to surgical assistance.
DeformerNet: A Deep Learning Approach to 3D Deformable Object Manipulation
In this paper, we propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet.
