基于不确定性驱动前瞻预测的自适应运动生成
Adaptive Motion Generation Using Uncertainty-Driven Foresight Prediction
AI总结：
本文提出一种基于不确定性驱动前瞻预测的自适应运动生成方法，通过采样多个可能未来并选择低不确定性未来细化隐藏状态，在开门任务中实现了传统方法无法稳定实现的运动自适应分化。
摘要：
在执行真实世界机器人任务时，环境不确定性长期以来一直是难以处理的特性。这是因为不确定性会产生无法被人工脚本覆盖的意外观测。基于学习的机器人控制方法是一种针对未知情况生成灵活运动的有前景的方法，但由于其确定性本质，在不确定性下仍然容易受到影响。为了在此类条件下自适应地执行目标任务，机器人控制模型必须能够准确理解可能的不确定性，并以探索性方式推导出最小化此类不确定性的最优动作。本文扩展了一种现有的基于预测学习的机器人控制方法，该方法采用动态内部模拟进行前瞻预测。前瞻模块通过采样多个可能的未来并替换为导致较低未来不确定性的未来，来细化模型的隐藏状态。该模型的自适应性在一个开门任务上进行了评估。门可以通过推、拉或滑动方式打开，但机器人无法通过视觉区分是哪一种方式，因此需要即时适应。结果表明，所提出的模型通过与门的交互自适应地分化其运动，而传统方法无法稳定地分化。模型在RNN隐藏状态的Lyapunov指数上进行了分析，该指数反映了任务执行过程中每个时间步可能的发散情况。结果表明，前瞻模块使模型偏向于考虑未来后果，从而将不确定性嵌入到机器人控制器的策略中，而不是嵌入到最终观测中。这有利于实现自适应行为，即在探索过程中推导出多样化运动。
英文摘要：
Uncertainty of environments has long been a difficult characteristic to handle, when performing real-world robot tasks. This is because the uncertainty produces unexpected observations that cannot be covered by manual scripting. Learning based robot controlling methods are a promising approach for generating flexible motions against unknown situations, but still tend to suffer under uncertainty due to its deterministic nature. In order to adaptively perform the target task under such conditions, the robot control model must be able to accurately understand the possible uncertainty, and to exploratively derive the optimal action that minimizes such uncertainty. This paper extended an existing predictive learning based robot control method, which employ foresight prediction using dynamic internal simulation. The foresight module refines the model's hidden states by sampling multiple possible futures and replace with the one that led to the lower future uncertainty. The adaptiveness of the model was evaluated on a door opening task. The door can be opened either by pushing, pulling, or sliding, but robot cannot visually distinguish which way, and is required to adapt on the fly. The results showed that the proposed model adaptively diverged its motion through interaction with the door, whereas conventional methods failed to stably diverge. The models were analyzed on Lyapunov exponents of RNN hidden states which reflect the possible divergence at each time step during task execution. The result indicated that the foresight module biased the model to consider future consequences, which lead to embedding uncertainties at the policy of the robot controller, rather than the resultant observation. This is beneficial for implementing adaptive behaviors, which indices derivation of diverse motion during exploration.
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。
