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Greatest papers with code

SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation

14 Nov 2020Xingyu-Lin/softgym

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.

DEFORMABLE OBJECT MANIPULATION OPENAI GYM

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

20 Jun 2018JanMatas/Rainbow_ddpg

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.

DEFORMABLE OBJECT MANIPULATION

PLAS: Latent Action Space for Offline Reinforcement Learning

14 Nov 2020polixir/OfflineRL

The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment.

CONTINUOUS CONTROL DEFORMABLE OBJECT MANIPULATION

Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

24 Nov 2020WanquanF/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

29 Oct 2019wilson1yan/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.

DEFORMABLE OBJECT MANIPULATION

Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making

26 Sep 2018DanielTakeshi/IL_ROS_HSR

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