Benchmarks
Greatest papers with code
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Further, we evaluate a variety of algorithms on these tasks and highlight challenges for reinforcement learning algorithms, including dealing with a state representation that has a high intrinsic dimensionality and is partially observable.
Sim-to-Real Reinforcement Learning for Deformable Object Manipulation
Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world.
PLAS: Latent Action Space for Offline Reinforcement Learning
The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment.
Recurrent Multi-view Alignment Network for Unsupervised Surface Registration
• WanquanF/RMA-Net
• 
Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data.
DEFORMABLE OBJECT MANIPULATION NEURAL RENDERING POINT CLOUD REGISTRATION
Learning to Manipulate Deformable Objects without Demonstrations
• wilson1yan/rlpyt
•
Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on random pick points.
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points.
DECISION MAKING DEFORMABLE OBJECT MANIPULATION TRANSFER LEARNING