Our approach
Instead of learning to master specific tasks separately, Covariant robots learn general abilities such as robust 3D perception, physical affordances of objects, few-shot learning and real-time motion planning. This allows them to adapt to new tasks just like people do — by breaking down complex tasks into simple steps and applying general skills to complete them.
Bringing practical AI Robotics into the physical world is hard. It involves giving robots a level of autonomy that requires breakthroughs in AI research. That’s why we have assembled a team that has published cutting-edge research papers at the top AI conferences and journals, with more than 50,000 collective citations. In addition to our research, we’ve also brought together a world-class engineering team to create new types of highly robust, reliable and performant cyber-physical systems.
We’re only as strong as our weakest link. If one component lags, the whole system fails. That’s why we’ve built a full-stack team, investing in making each robotic component world-class, from software systems to hardware stations, from AI algorithms to end-effectors.
Building on the team's academic research
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2020
Embodied Language Grounding with Implicit 3D Visual Feature Representations
Mihir Prabhudesai, Hsiao-Yu Fish Tung, Syed Ashar Javed, Maximilian Sieb, Adam W. Harley, Katerina Fragkiadaki
International Conference on Robotics and Automation (ICRA), Paris, France, May 2020
Guided Uncertainty Aware Policy Optimization: Combining Model-Free and Model-Based Strategies for Sample-Efficient Learning
Michelle Lee*, Carlos Florensa*, Jonathan Tremblay, Nathan Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox
International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020
Subpolicy Adaptation for Hierarchical Reinforcement Learning
Alexander Li*, Carlos Florensa*, Ignasi Clavera, Pieter Abbeel
Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019
Goal Conditioned Imitation Learning
Yiming Ding*, Carlos Florensa*, Mariano Phielipp, Pieter Abbeel
International Conference on Machine Learning (ICML), Long Beach, USA, June 2019
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
D. Ho, E. Liang, I. Stoica, P. Abbeel, X. Chen
Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018
Evolved Policy Gradients
Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel
IEEE-RAS International Conference on Humanoid Robots (Humanoids), Beijing, China, November 2018
Data Dreaming for Object Detection: Learning Object-Centric State Representations for Visual Imitation
Maximilian Sieb, Katerina Fragkiadaki
IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS), Madrid, Spain, October 2018
Domain Randomization and Generative Models for Robotic Grasping
Joshua Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel
Want to join our team?
Interested in working at Covariant? We’re hiring!
