Due to multiple requests we have extended the submission deadline for the NeurIPS2021 - 4th Robot Learning Workshop to the 30 September 2021 (Anywhere on Earth).
If you have already submitted your work, you will be able to adapt and improve your submission until the new deadline!
Please feel free to find further information on our website http://www.robot-learning.ml/2021/
or reach out for any questions you might have.
Recently, progress in machine learning has enabled robots to demonstrate strong performance in helping humans across many fields and applications such as manufacturing, logistics, and transportation. While these results are promising, access to high-quality, task-relevant data remains one of the largest bottlenecks for the successful deployment of such technologies in the real world. For these reasons, applying machine learning to real-world robotic systems has naturally become an important part of the NeurIPS community. Today, unique opportunities are presenting themselves in this quest for robust, efficient, and continuous learning.
Large-scale, self-supervised, and multimodal approaches to learning are increasing data efficiency for supervised approaches. Similarly, reinforcement and imitation learning are becoming more stable and data-efficient in real-world settings; new approaches combining strong, principled safety and stability guarantees with the expressive power of machine learning are emerging. Methods to generate, re-use, and integrate more sources of valuable data, such as lifelong learning, transfer, and other forms of continuous improvement could unlock the next steps of performance. However, accessing these data sources comes with fundamental challenges, which include safety, stability, and the daunting issue of providing supervision for learning while the robot is in operation.
This workshop aims to discuss how these emerging trends in machine learning of self-supervision and lifelong learning can be best utilized in real-world robotic systems. We bring together experts with diverse perspectives on this topic to highlight the ways current successes in the field are changing the conversation around lifelong learning, and how this will affect the future of robotics, machine learning, and our ability to deploy intelligent, self-improving agents to enhance people’s lives. We encourage researchers, especially early-stage researchers to contribute their recent findings.
Topics of interest include but are not limited to:
- Challenges in real-world application of machine learning in robot perception and decision-making
- Lifelong learning and adaptation for robots
- Data-efficiency via transfer, multitask, and meta learning
- Understanding, quantifying, and bridging the simulation-to reality-gap
- Uncertainty, robustness, and safety
- Self-supervised and semi-supervised representation learning
- Predictive coding
- Environment prediction
- Occlusion inference
- Long-horizon task learning
- Demonstration-based and goal-oriented policy learning
- Reward specification or learning
- Online or active learning for system identification and adaptation to a changing dynamics
- Self-supervised skill acquisition via self-play and student-teacher approaches
- Transfer learning across robot morphologies
- Architectures for open-ended learning
- Active perception
- Scene interpretation
Submission website: https://cmt3.research.microsoft.com/NeurIPSWRL2021/
Submissions should use the NeurIPS template, and be 4 pages
(plus as many pages as necessary for references). The reviewing process
will be double blind following the same standards as the main conference.
Accepted papers and eventual supplementary material will be made available on the workshop website. However, this does not constitute an archival publication and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conferences. In the spirit of providing useful feedback, we will not accept submissions accepted to other conference or journal proceedings at the time of submission.