Deformable Object Manipulation

8 papers with code • 0 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

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

DanielTakeshi/IL_ROS_HSR 26 Sep 2018

We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points.

PLAS: Latent Action Space for Offline Reinforcement Learning

takuseno/d3rlpy 14 Nov 2020

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

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

JanMatas/Rainbow_ddpg 20 Jun 2018

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.

Learning to Manipulate Deformable Objects without Demonstrations

wilson1yan/rlpyt 29 Oct 2019

Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on random pick points.

Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

wilson1yan/contrastive-forward-model 11 Mar 2020

Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models.

SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation

Xingyu-Lin/softgym 14 Nov 2020

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.

Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

WanquanF/RMA-Net CVPR 2021

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.

Imitation Learning via Differentiable Physics

sail-sg/ild 10 Jun 2022

With the physics prior, ILD policies can not only be transferable to unseen environment specifications but also yield higher final performance on a variety of tasks.