Register now After registration you will be able to apply for this opportunity online.
Learning State Estimation for Locomotion
Model-based state estimation for locomotion has shown some significant drawbacks, especially in the case of complex contact scenarios. At the same time, locomotion controllers are evolving, now purposely using knee contacts or wheel slippage for advanced motions. The current model-based state estimation techniques often cannot supply sufficiently accurate observations for these controllers, leading to major estimation drifts and thus potential failures. In this project, we aim to leverage learning-based methods not only for locomotion control, but also for state estimation. Preliminary work shows that creating a state estimation through supervised learning from recorded simulation data can produce a viable solution. Furthermore, fusing these approaches with classical filtering theory opens a promising realm of research. The project should also compare the developed methods with existing approaches on real hardware. If time permits, we are interested in learning state estimation and locomotion jointly.
Keywords: State Estimation
Machine Learning
Legged Locomotion
Related literature
Buchanan, Russell, et al. "Learning inertial odometry for dynamic legged robot state estimation." Conference on robot learning. PMLR, 2022.
Related literature
Buchanan, Russell, et al. "Learning inertial odometry for dynamic legged robot state estimation." Conference on robot learning. PMLR, 2022.
Literature research
Develop a learning-based state estimator in simulation
Validate and compare state estimation on real hardware
Literature research
Develop a learning-based state estimator in simulation
Validate and compare state estimation on real hardware
Highly motivated student eager to test on real hardware
Comfortable with Python, C++, and ROS stacks
Familiar with basic concepts of RL, ML
Familiar with basic concepts of state estimation and sensor models
Highly motivated student eager to test on real hardware
Comfortable with Python, C++, and ROS stacks
Familiar with basic concepts of RL, ML
Familiar with basic concepts of state estimation and sensor models