Self-Supervised Goal-Conditioned Pick and Place
arXiv:2008.11466
Abstract
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.
In RSS 2020 Visual Learning and Reasoning for Robotic Manipulation Workshop
References in corpus (5)
- Adam: A Method for Stochastic Optimization
- Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
- Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly
- Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
- Visual Task Progress Estimation with Appearance Invariant Embeddings for Robot Control and Planning