Participants: Univ.-Prof. Dr. Elmar Rueckert, Vedant Dave
Agenda
Finalise the formulation of Iterative Empowerment and implement it.
Complete the Information Bottleneck formulation.
Topic 1: Iterative Empowerment
Finalise the formulation.
Implement the formulation.
Topic 2: Information bottleneck
Continue on the current formulation.
Literature
To be added
Next Meeting
TBD
15.02.2023 Meeting Notes
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Meeting Details
Date: 15th February 2022
Time : 13:30 – 14:00
Location : Chair of CPS, Montanuniverität Leoben
Participants: Univ.-Prof. Dr. Elmar Rueckert, Vedant Dave
Agenda
Check the formulation of Iterative Empowerment.
Information Bottleneck for Non-Markovian environments.
Topic 1: Iterative Empowerment
Implementation of formulation in gridworld.
Comparision with prior approaches and other curiosity modules.
Topic 2: Information bottleneck for Non-Markovian environments
Idea formulation.
Study Information bottleneck and related papers thotoughly.
Literature
To be added
Next Meeting
TBD
04.11.2022 Meeting Notes
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Meeting Details
Date: 3rd October 2022
Time : 08:30 – 09:00
Location : Chair of CPS, Montanuniverität Leoben
Participants: Univ.-Prof. Dr. Elmar Rueckert, Vedant Dave
Agenda
Extension idea formulation for Journal Paper
Work on exploration and curiosity
Topic 1: Science Robotics Paper
Bi-level Probabilistic Movement Primitives due to uneven error propagation in different stages.
Read literature [1] and see if we find something.
Topic 3: Dynamic Exploration and Curiosity
We got out baseline [2] and now we try to implement this paper.
Try the same model on more complex environments and more out-of-box goals.
In process, first implement [3].
Literature
R. Lioutikov, G. Maeda, F. Veiga, K. Kersting and J. Peters, “Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives,” 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 5651-5658, doi: 10.1109/ICRA.2018.8460190.
Mendonca, Russell, et al. “Discovering and achieving goals via world models.” Advances in Neural Information Processing Systems 34 (2021): 24379-24391.
Hafner, Danijar, et al. “Learning latent dynamics for planning from pixels.” International conference on machine learning. PMLR, 2019.
Next Meeting
TBD
30.09.2021 Meeting Notes
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Meeting Details
Date : 30th September 2022
Time : 11:30 – 12:30
Location : Chair of CPS, Montanuniverität Leoben
Participants: Univ.-Prof. Dr. Elmar Rueckert, Vedant Dave
Agenda
Humanoids paper ready from Reviewers comments
Extend the Conference paper for the Journal
Active Exploration with Forward and Inverse Model learning
Topic 1: Humanoids Paper
Change the paper according to the reviews.
Add Real-world Experiments.
Topic 2: Science Robotics Paper
Extend the paper for learning objects at different locations.
Conduct experiments with multiple objects on the table.
Enable object tracking and extend it.
Extension to Riemannian Manifold to reduce the Orientation errors.
Topic 3: Active Exploration
Survey on Exploration strategies and Empowerment.
Trying to work on relationships between Maximum Entropy of Latent variables and Tasks.
Trying to find literature on Learning Phase Jumps.
Goal Babbling.
Literature
Inverse Dynamic Predictions
S. Bechtle, B. Hammoud, A. Rai, F. Meier and L. Righetti, “Leveraging Forward Model Prediction Error for Learning Control,” 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 4445-4451, doi: 10.1109/ICRA48506.2021.9561396..
Eysenbach, Benjamin, et al. “Diversity is all you need: Learning skills without a reward function.” arXiv preprint arXiv:1802.06070 (2018).
Klyubin, Alexander S., Daniel Polani, and Chrystopher L. Nehaniv. “All else being equal be empowered.” European Conference on Artificial Life. Springer, Berlin, Heidelberg, 2005.
Next Meeting
TBD
13.09.2021 Meeting Notes
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Meeting Details
Date : 13th September 2022
Time : 12:30 – 1:30
Location : Chair of CPS, Montanuniverität Leoben
Participants: Univ.-Prof. Dr. Elmar Rueckert, Vedant Dave
Agenda
Learning Consistent Forward and Inverse Dynamics.
Topic 1: Idea Development
Thinking in terms of Closed loop systems and feedback controllers.
Regularizing Forward model via Inverse model.
Single-step and Multi-step prediction models.
Comparing Multi-step predictions with Movement Primitives.
Topic 2: Toy Example
Generate a toy dataset(Temperature) with just single parameter.
Try forward model to approximate out-of-distribution testing data.
If it fails, try to regularize it with Inverse model and check if it works out.
Literature
Inverse Dynamic Predictions
Cooper, Richard. (2010). Forward and Inverse Models in Motor Control and Cognitive Control. Proceedings of the International Symposium on AI Inspired Biology – A Symposium at the AISB 2010 Convention.
Moore, Andrew. “Fast, robust adaptive control by learning only forward models.” Advances in neural information processing systems 4 (1991).
Topic 1: Learning Forward and Inverse Dynamics with Cycle Consistency
Develop a framework to learn forward and inverse model of the system simultaneously.
Search tasks where both models are required.
Test on the datatset from [1].
Topic 2: Binding Simulation and Reality Gap
Working with forward model in the simulation and correcting it with inverse model from real system.
Develop tasks for contact
Literature
Inverse Dynamic Predictions
Elmar Rueckert et al. “Learning inverse dynamics models in O(n) time with LSTM networks”. In: 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). 2017, pp. 811–816.
Vaisakh Shaj et al. “Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning”. In: Proceedings of the 2020 Conference on Robot Learning. Ed. by Jens Kober, Fabio Ramos, and Claire Tomlin. Vol. 155. Proceedings of Machine Learning Research. PMLR, Nov. 2021, pp. 765–781.
Moritz Reuss et al. “End to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control”. In: Proceedings of Robotics: Science and Systems. New York City, NY, USA, June 2022.