In this paper, we propose a novel lightweight learning from demonstration (LfD) model based on reservoir computing that can learn and generate multiple movement trajectories with prediction intervals, which we call as Context-based Echo State Network with prediction confidence (CESN+). CESN+ can generate movement trajectories that may go beyond the initial LfD training based on a desired set of conditions while providing confidence on its generated output. To assess the abilities of CESN+, we first evaluate its performance against Conditional Neural Movement Primitives (CNMP), a comparable framework that uses a conditional neural process to generate movement primitives. Our findings indicate that CESN+ not only outperforms CNMP but is also faster to train and demonstrates impressive performance in generating trajectories for extrapolation cases. In human-robot shared control applications, the confidence of the machine generated trajectory is a key indicator of how to arbitrate control sharing. To show the usability of the CESN+ for human-robot adaptive shared control, we have designed a proof-of-concept human-robot shared control task and tested its efficacy in adapting the sharing weight between the human and the robot by comparing it to a fixed-weight control scheme. The simulation experiments show that with CESN+ based adaptive sharing the total human load in shared control can be significantly reduced. Overall, the developed CESN+ model is a strong lightweight LfD system with desirable properties such fast training and ability to extrapolate to the new task parameters while producing robust prediction intervals for its output.

翻译：本文提出了一种基于储层计算的新型轻量级示教学习模型，该模型能够学习并生成带有预测区间的多运动轨迹，我们将其称为基于上下文的预测置信度回声状态网络。该模型能够基于期望条件集生成可能超越初始示教训练范围的运动轨迹，同时为其输出提供置信度评估。为验证该模型的性能，我们首先将其与条件神经运动基元进行比较评估，后者是使用条件神经过程生成运动基元的对标框架。实验结果表明，该模型不仅在性能上优于条件神经运动基元，而且具有更快的训练速度，在轨迹外推生成任务中展现出卓越性能。在人机共享控制应用中，机器生成轨迹的置信度是决定控制权分配的关键指标。为验证该模型在人机自适应共享控制中的实用性，我们设计了概念验证型人机共享控制任务，通过与固定权重控制方案的对比实验，测试了其在调节人机控制权重分配方面的有效性。仿真实验表明，采用基于该模型的自适应共享方案能显著降低共享控制中操作者的总体负荷。总体而言，所开发的模型是一种功能强大的轻量级示教学习系统，具备训练速度快、能适应新任务参数外推等优良特性，同时能为输出提供稳健的预测区间。