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Found 4 papers, 1 papers with code
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.
GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy
To achieve this, we augment the policy by conditioning it on deformable rope parameters and training it with a diverse range of simulated deformable ropes so that the policy can adjust actions based on different rope parameters.
Collective Intelligence for 2D Push Manipulations with Mobile Robots
While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems.
Deep Billboards towards Lossless Real2Sim in Virtual Reality
An aspirational goal for virtual reality (VR) is to bring in a rich diversity of real world objects losslessly.
