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.