Self-Supervised Goal-Conditioned Pick and Place
By Coline Devin, Payam Rowghanian, Christopher M. Vigorito, William D. Richards, Khashayar Rohanimanesh. 2020
10 citations recorded by the source corpus.
Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious \emph{how} to learning from autonomously collected data without human-labeled supervision. In this work we learn pixel-wise object representations from unsupervised pick and place data that generalize to new objects. We introduce a novel framework for using these representations in order to predict where to pick and where to place in order to match a goal image. Finally, we demonstrate the utility of our approach in a simulated grasping environment.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yuke Zhu
- Li Fei-Fei
- Anima Anandkumar
- Sergey Levine
- Andy Zeng
- Linxi (Jim) Fan
- Jonathan Tompson
- Peter R. Florence
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Li Fei-Fei
- Jitendra Malik
- Trevor Darrell
- Dacheng Tao
- Xiaogang Wang
- Andrew Zisserman
- Alan L. Yuille
In Control and Systems Engineering
- Li Fei-Fei
- Yuke Zhu
- Linxi (Jim) Fan
- Andy Zeng
- Johnny Lee
- Anima Anandkumar
- Jonathan Tompson
- Peter R. Florence
Citations in the corpus
- Transporter Networks: Rearranging the Visual World for Robotic Manipulation (2020, background): “, logistics, agriculture, household) – leading to renewed interest in leveraging learned models to achieve general pick-and-place policies [23, 24, 25, 26, 27, 28, 29] that can handle unseen objects.”
- VIMA: General Robot Manipulation with Multimodal Prompts (2022, background): “While “pick-and-place” and “wipe” seem simple, they do cover a wide range of tabletop manipulation tasks and are crucial to industrial use cases like warehouse robots (Yoon et al., 2003; Berscheid et al., 2020; Devin et al., 2020; Song et al., 2019).”