Concetti Chiave
An algorithm for actively selecting trajectories to efficiently learn a model precondition for an inaccurate pre-specified dynamics model, addressing challenges arising from the sequential nature of trajectories and prioritizing task-relevant data.
Sintesi
The paper presents an approach for actively learning model preconditions to improve planning with inaccurate dynamics models. The key insights are:
The authors formulate the problem of actively learning model preconditions, where the goal is to efficiently select trajectories to train a Model Deviation Estimator (MDE) that can define regions of state-action space where the dynamics model is accurate enough for planning.
They propose techniques to address challenges in this problem, such as the sequential nature of trajectories and the need to prioritize task-relevant data. This includes generating candidate trajectories that satisfy goal constraints, and using a multi-step acquisition function to compute the utility of a trajectory.
The experimental analysis shows the benefits of the proposed active learning approach compared to baselines in three planning scenarios: an icy gridworld, a simulated plant watering task, and a real-world plant watering task. The results demonstrate an improvement of approximately 80% in success rate after only four real-world trajectories when using the proposed techniques.
The authors analyze how different algorithmic choices, such as the acquisition function and candidate trajectory generation, affect the performance in terms of model precondition accuracy, planning success rate, and end-to-end task success.
Overall, the paper presents an effective approach for actively learning model preconditions to enable reliable planning with inaccurate dynamics models, particularly in the context of manipulation tasks with variable-accuracy models.
Statistiche
The paper does not provide specific numerical data in the form of sentences. The key results are presented in the form of plots and qualitative analysis.
Citazioni
The paper does not contain any striking quotes that support the key logics.