Sufficiently Accurate Model Learning
This work addresses the need for better model quality in robotics, offering an incremental improvement for control and planning tasks.
The paper tackles the problem of improving robot control and planning by learning dynamics models with predictable error characteristics, proposing a primal-dual method to enforce constraints on error in specific state-space regions, resulting in more reliable models for algorithms.
Modeling how a robot interacts with the environment around it is an important prerequisite for designing control and planning algorithms. In fact, the performance of controllers and planners is highly dependent on the quality of the model. One popular approach is to learn data driven models in order to compensate for inaccurate physical measurements and to adapt to systems that evolve over time. In this paper, we investigate a method to regularize model learning techniques to provide better error characteristics for traditional control and planning algorithms. This work proposes learning "Sufficiently Accurate" models of dynamics using a primal-dual method that can explicitly enforce constraints on the error in pre-defined parts of the state-space. The result of this method is that the error characteristics of the learned model is more predictable and can be better utilized by planning and control algorithms. The characteristics of Sufficiently Accurate models are analyzed through experiments on a simulated ball paddle system.