用于机器人技能精度保持外推的约束方程学习器网络
Constrained Equation Learner Networks for Precision-Preserving Extrapolation of Robotic Skills
AI总结：
针对示教编程中机器人技能外分布适配及精度保持难题，提出约束方程学习器网络框架，以学习到的解析表达式为基函数做约束回归，经仿真与实物实验验证，其泛化和适配性能优于现有方法。
摘要：
在示教编程（Programming by Demonstration）中，机器人从人类示教中学习新技能。学习完成后，机器人不仅应能复现该技能，还应能在无需收集新训练数据的情况下将其泛化到偏移域。现有文献已对相似域的适配展开研究，但一个待解决的问题是如何将习得的技能适配到数据分布之外的不同条件，更重要的是如何保持所需适配的精度。本文提出一种名为Constrained Equation Learner Networks（约束方程学习器网络）的新型监督学习框架，从约束回归的角度解决示教编程中的轨迹适配问题。传统约束回归方法使用一类基函数（例如高斯函数），而我们利用Equation Learner Networks（方程学习器网络）学习一组解析表达式，并将其用作基函数。这些基函数从示教数据中学习，目标是最小化与训练数据的偏差，同时施加代表所需适配的约束，例如新的初始点或终点，或是将轨迹维持在给定范围内。我们的方法解决了机器人轨迹适配中的三大核心难题：1）最小化新适配带来的轨迹失真；2）保持适配的精度；3）应对基函数结构缺乏先验认知的问题。我们在一系列因环境变化需要适配的机器人任务中，通过仿真和实物实验验证了所提方法，并将所得结果与两种现有方法进行了对比。实验结果表明，Constrained Equation Learner Networks通过提升机器人技能的泛化性和适配性，性能优于现有最优方法。
英文摘要：
In Programming by Demonstration, the robot learns novel skills from human demonstrations. After learning, the robot should be able not only to reproduce the skill, but also to generalize it to shifted domains without collecting new training data. Adaptation to similar domains has been investigated in the literature; however, an open problem is how to adapt learned skills to different conditions that are outside of the data distribution, and, more important, how to preserve the precision of the desired adaptations. This paper presents a novel supervised learning framework called Constrained Equation Learner Networks that addresses the trajectory adaptation problem in Programming by Demonstrations from a constrained regression perspective. While conventional approaches for constrained regression use one kind of basis function, e.g., Gaussian, we exploit Equation Learner Networks to learn a set of analytical expressions and use them as basis functions. These basis functions are learned from demonstration with the objective to minimize deviations from the training data while imposing constraints that represent the desired adaptations, like new initial or final points or maintaining the trajectory within given bounds. Our approach addresses three main difficulties in adapting robotic trajectories: 1) minimizing the distortion of the trajectory for new adaptations; 2) preserving the precision of the adaptations; and 3) dealing with the lack of intuition about the structure of basis functions. We validate our approach both in simulation and in real experiments in a set of robotic tasks that require adaptation due to changes in the environment, and we compare obtained results with two existing approaches. Performed experiments show that Constrained Equation Learner Networks outperform state of the art approaches by increasing generalization and adaptability of robotic skills.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
