# Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation

arXiv: https://arxiv.org/abs/2304.08703 · HTML: https://mlsanity.com/paper/2304.08703/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale](https://mlsanity.com/paper/2104.08212/) (2021)
- [Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation](https://mlsanity.com/paper/1806.08756/) (2018)
- [RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real](https://mlsanity.com/paper/2006.09001/) (2020)
- [Unseen Object Instance Segmentation for Robotic Environments](https://mlsanity.com/paper/2007.08073/) (2020)
- [RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer](https://mlsanity.com/paper/2011.03148/) (2020)
- [Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation](https://mlsanity.com/paper/2007.15157/) (2020)
- [Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data](https://mlsanity.com/paper/2003.01835/) (2020)
- [Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop](https://mlsanity.com/paper/2012.03806/) (2020)
- [What Can I Do Here? Learning New Skills by Imagining Visual Affordances](https://mlsanity.com/paper/2106.00671/) (2021)
- [Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation](https://mlsanity.com/paper/1910.09470/) (2019)
- [Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications](https://mlsanity.com/paper/2102.08096/) (2021)

## Built on by (most cited first, 2 of 2)

- [Cycle-Correspondence Loss: Learning Dense View-Invariant Visual Features from Unlabeled and Unordered RGB Images](https://mlsanity.com/paper/2406.12441/) (2024)
- [Multi-Agent Transfer Learning via Temporal Contrastive Learning](https://mlsanity.com/paper/2406.01377/) (2024)

## Abstract (arXiv)

It is crucial to address the following issues for ubiquitous robotics manipulation applications: (a) vision-based manipulation tasks require the robot to visually learn and understand the object with rich information like dense object descriptors; and (b) sim-to-real transfer in robotics aims to close the gap between simulated and real data. In this paper, we present Sim-to-Real Dense Object Nets (SRDONs), a dense object descriptor that not only understands the object via appropriate representation but also maps simulated and real data to a unified feature space with pixel consistency. We proposed an object-to-object matching method for image pairs from different scenes and different domains. This method helps reduce the effort of training data from real-world by taking advantage of public datasets, such as GraspNet. With sim-to-real object representation consistency, our SRDONs can serve as a building block for a variety of sim-to-real manipulation tasks. We demonstrate in experiments that pre-trained SRDONs significantly improve performances on unseen objects and unseen visual environments for various robotic tasks with zero real-world training.