Modeling, learning, perception, and control methods for deformable object manipulation

H Yin, A Varava, D Kragic - Science Robotics, 2021 - science.org
Perceiving and handling deformable objects is an integral part of everyday life for humans.
Automating tasks such as food handling, garment sorting, or assistive dressing requires …

Embodiment in socially interactive robots

E Deng, B Mutlu, MJ Mataric - Foundations and Trends® in …, 2019 - nowpublishers.com
Physical embodiment is a required component for robots that are structurally coupled with
their real-world environments. However, most socially interactive robots do not need to …

Softgym: Benchmarking deep reinforcement learning for deformable object manipulation

X Lin, Y Wang, J Olkin, D Held - Conference on Robot …, 2021 - proceedings.mlr.press
Manipulating deformable objects has long been a challenge in robotics due to its high
dimensional state representation and complex dynamics. Recent success in deep …

Learning to rearrange deformable cables, fabrics, and bags with goal-conditioned transporter networks

D Seita, P Florence, J Tompson… - … on Robotics and …, 2021 - ieeexplore.ieee.org
Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a
long-standing challenge in robotic manipulation. The complex dynamics and high …

Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

J Sanchez, JA Corrales… - … Journal of Robotics …, 2018 - journals.sagepub.com
We present a survey of recent work on robot manipulation and sensing of deformable
objects, a field with relevant applications in diverse industries such as medicine (eg surgical …

Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding

H Ha, S Song - Conference on Robot Learning, 2022 - proceedings.mlr.press
High-velocity dynamic actions (eg, fling or throw) play a crucial role in our everyday
interaction with deformable objects by improving our efficiency and effectively expanding our …

Antipodal robotic grasping using generative residual convolutional neural network

S Kumra, S Joshi, F Sahin - 2020 IEEE/RSJ International …, 2020 - ieeexplore.ieee.org
In this paper, we present a modular robotic system to tackle the problem of generating and
performing antipodal robotic grasps for unknown objects from the n-channel image of the …

Sim-to-real reinforcement learning for deformable object manipulation

J Matas, S James, AJ Davison - Conference on Robot …, 2018 - proceedings.mlr.press
We have seen much recent progress in rigid object manipulation, but interaction with
deformable objects has notably lagged behind. Due to the large configuration space of …

Robotic grasp detection using deep convolutional neural networks

S Kumra, C Kanan - … on Intelligent Robots and Systems (IROS), 2017 - ieeexplore.ieee.org
Deep learning has significantly advanced computer vision and natural language processing.
While there have been some successes in robotics using deep learning, it has not been …

kpam: Keypoint affordances for category-level robotic manipulation

L Manuelli, W Gao, P Florence, R Tedrake - The International Symposium …, 2019 - Springer
We would like robots to achieve purposeful manipulation by placing any instance from a
category of objects into a desired set of goal states. Existing manipulation pipelines typically …