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Found 4 papers, 1 papers with code
Investigating Compounding Prediction Errors in Learned Dynamics Models
In this paper, we explore the effects of subcomponents of a control problem on long term prediction error: including choosing a system, collecting data, and training a model.
Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning
As a comparison to the significant engineering effort required for an analytic control law, we implement a data-driven model-based reinforcement learning yaw controller in a simulated flight task.
Robotics
Data-efficient Learning of Morphology and Controller for a Microrobot
Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design.
Learning Flexible and Reusable Locomotion Primitives for a Microrobot
This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multi-objective locomotion primitives that can be used for planning.
