S&AS: FND: COLLAB: Probabilistic Underactuated Motion Adaptation

非传统的、欠驱动的机器人，如更广泛的人形机器人或有腿的平台，提供了在有限的三维环境中移动和执行工作的潜力，这是目前现有的自主代理无法实现的。然而，这种潜力在很大程度上尚未实现，因为很难可靠地调整这些平台的行为，以适应“现实世界”中不断变化和不确定的任务和环境条件。尽管许多管理当代任务和运动规划技术的基本原则适用于不同的平台，但这些原则的实际实施在很大程度上是平台特定的。相比之下，本项目将采用概率规划框架，该框架学习执行相关任务的不同平台的运动模式的共同结构，然后使用该结构生成广义的、内在独立于平台的运动原语。在运行时，这些原语将根据当地任务和环境条件对特定的机器人模型进行必要的接地和调整。该项目的主要好处将是增加自动平台在城市搜索和救援、工业检查和行星探测等任务中的效用。将开发的分析技术将对运动科学和基于学习的运动协调方法产生进一步的影响。此外，pi还将参与K-12的外展活动，包括FIRST机器人竞赛和罗切斯特博物馆和科学中心的机器人演示。该项目将特别解决欠驱动机器人在不同目标和环境条件分布下的实时任务和运动规划可追溯性的基本限制。将开发概率模型来有效地推理和适应不同高关节、欠驱动机器人的名义行为。行为推断将使1)选择适当的预先存在的行为（在项目过程中开发）成为可能，2)使用名义行为的新组合来形成复合的，特定于任务的行为，以及3)利用相似的，但不一定相同的，跨异构平台的运动学结构来在它们之间转移行为。为了确保所开发模型的实际在线实现的成功，pi将开发结合概率推理、非线性降维和动态运动原语的算法，以产生高效运动生成和鲁棒在线适应的新组合。除了不同的任务和环境条件外，还将探讨概率模型对机器人平台内部运动学和动力学变化的适应性，例如由于运动性能下降或关节或整个肢体的结构故障而引起的变化。这些模型将在卡内基梅隆大学六足机器人和Robotis OP2两个物理平台上进行模拟和实验结果的结合训练和验证。此外，pi将开发软件工具并发布与欠驱动系统运动适应的可推广概率模型相关的开源产品。

Unconventional, underactuated robots, such as humanoids or legged platforms more broadly, offer the potential to move through and perform work in constrained, three-dimensional environments that are currently inaccessible to existing autonomous agents. However, this potential has been largely unrealized because it is difficult to reliably adapt the behaviors of these platforms to account for the changing and uncertain task and environmental conditions in the "real world." Although many of the fundamental principles that govern contemporary task and motion planning techniques are applicable across different platforms, the practical implementation of these principles has been largely platform specific. In contrast, this project will adopt a probabilistic planning framework which learns common structure for the motion patterns of different platforms performing related tasks, then uses this structure to generate generalized, inherently platform independent, motion primitives. At runtime, the primitives will be grounded and adapted where necessary to specific robot models given local task and environmental conditions. The primary benefit of this project will be an increase in the utility of autonomous platforms for tasks such as urban search and rescue, industrial inspection, and planetary exploration. The analytical techniques that will be developed will have further impacts on locomotion science and learning-based approaches to motion coordination. The PIs will additionally be involved with K-12 outreach involving robot demonstrations at FIRST Robotics Competitions and the Rochester Museum and Science Center.This project will specifically address fundamental limitations in the tractability of real-time task and motion planning for underactuated robots over diverse objectives and distributions of environmental conditions. Probabilistic models will be developed to efficiently reason over and adapt the nominal behaviors of different highly-articulated, underactuated robots. The behavioral inference will make it possible to 1) select appropriate pre-existing behaviors (developed over the course of the project) where relevant, 2) use novel combinations of nominal behaviors to form compound, task-specific behaviors, and 3) leverage similar, but not necessarily the same, kinematic structure across heterogeneous platforms to transfer behaviors between them. To ensure the success of the practical, online implementation of the developed models, the PIs will develop algorithms that combine probabilistic inference, nonlinear dimensionality reduction, and dynamic movement primitives to produce a novel combination of efficient motion generation and robust online adaptation. In addition to varying task and environmental conditions, the adaptability of the probabilistic models to changes in the internal kinematics and dynamics of robot platforms, such as those that would arise from degraded motor performance or structural failures of joints or entire limbs, will also be explored. The models will be trained and validated using a combination of simulation and experimental results on two physical platforms: the Carnegie Mellon Hexapod and the Robotis OP2. Furthermore, the PIs will develop software tools and release open-source products related to generalizable probabilistic models for motion adaptation of underactuated systems.