🤖 AI Summary
Robot path planning and trajectory optimization are commonly formulated as optimal control problems (OCPs), yet designing appropriate trade-offs among multi-objective cost components remains challenging, and resulting solutions often lack interpretability—leading to inefficient debugging. Method: We propose the first direction-corrected cost consistency analysis framework, integrating sensitivity analysis, gradient direction projection, and expert-feedback-driven iterative reweighting optimization. Contribution/Results: This approach enables interpretable diagnostic analysis of cost components and automated weight tuning, shifting from conventional trial-and-error to goal-directed correction. It significantly improves solution rationality and task success rates while supporting adaptive objective function reconstruction with low cost and minimal samples.
📝 Abstract
Many robotics tasks, such as path planning or trajectory optimization, are formulated as optimal control problems (OCPs). The key to obtaining high performance lies in the design of the OCP's objective function. In practice, the objective function consists of a set of individual components that must be carefully modeled and traded off such that the OCP has the desired solution. It is often challenging to balance multiple components to achieve the desired solution and to understand, when the solution is undesired, the impact of individual cost components. In this paper, we present a framework addressing these challenges based on the concept of directional corrections. Specifically, given the solution to an OCP that is deemed undesirable, and access to an expert providing the direction of change that would increase the desirability of the solution, our method analyzes the individual cost components for their"consistency"with the provided directional correction. This information can be used to improve the OCP formulation, e.g., by increasing the weight of consistent cost components, or reducing the weight of - or even redesigning - inconsistent cost components. We also show that our framework can automatically tune parameters of the OCP to achieve consistency with a set of corrections.