# Self-Supervised Goal-Conditioned Pick and Place

arXiv: https://arxiv.org/abs/2008.11466 · HTML: https://mlsanity.com/paper/2008.11466/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [VIMA: General Robot Manipulation with Multimodal Prompts](https://mlsanity.com/paper/2210.03094/) (2022)
- [Equivariant Transporter Network](https://mlsanity.com/paper/2202.09400/) (2022)
- [Leveraging Symmetries in Pick and Place](https://mlsanity.com/paper/2308.07948/) (2023)
- [Multi-Task Learning with Sequence-Conditioned Transporter Networks](https://mlsanity.com/paper/2109.07578/) (2021)
- [Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement](https://mlsanity.com/paper/2303.11373/) (2023)

## Abstract (arXiv)

Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious 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.