用于人机共享控制的、具备预测置信度的基于上下文的回声状态网络
Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control
AI总结：
提出轻量级储备池计算模型CESN+，可带预测区间地生成并外推运动轨迹；实验显示其优于CNMP、训练更快，并能在人机共享控制中依据置信度自适应分配权重、显著降低人员负荷。
摘要：
本文提出一种新颖的基于储备池计算的轻量级示教学习（LfD）模型，能够学习并生成多条带有预测区间的运动轨迹，我们将其称为具备预测置信度的基于上下文的回声状态网络（Context-based Echo State Network with prediction confidence，CESN+）。CESN+能够根据一组期望条件生成可能超出初始LfD训练范围的运动轨迹，同时为其生成的输出提供置信度。为评估CESN+的能力，我们首先将其与Conditional Neural Movement Primitives（CNMP）进行性能比较；CNMP是一个使用条件神经过程生成运动基元的可比框架。研究结果表明，CESN+不仅性能优于CNMP，而且训练速度更快，并在外推情形下的轨迹生成中表现出令人瞩目的性能。在人机共享控制应用中，机器生成轨迹的置信度是如何仲裁控制共享的关键指标。为展示CESN+在人机自适应共享控制中的可用性，我们设计了一个概念验证的人机共享控制任务，并通过将其与固定权重控制方案进行比较，测试了其在调整人与机器人之间共享权重方面的功效。仿真实验表明，采用基于CESN+的自适应共享，可显著降低共享控制中的总人员负荷。总体而言，所开发的CESN+模型是一个强大的轻量级LfD系统，具有训练快速、能够外推至新任务参数等理想特性，同时可为其输出生成稳健的预测区间。
英文摘要：
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.
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。
