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1. Human-Robot Collaboration and Dialogue for Fault Recovery on Hierarchical Tasks

   https://doi.org/10.1007/978-3-030-62056-1_13  

   Blankenburg, J.; Zagainova, M.; Simmons, S.M.; Talavera, G.; Nicolescu, M.; Feil-Seifer, D. (October 2020, International Conference on Social Robotics)

   null (Ed.)

   Robotic systems typically follow a rigid approach to task execution, in which they perform the necessary steps in a specific order, but fail when having to cope with issues that arise during execution. We propose an approach that handles such cases through dialogue and human-robot collaboration. The proposed approach contributes a hierarchical control architecture that 1) autonomously detects and is cognizant of task execution failures, 2) initiates a dialogue with a human helper to obtain assistance, and 3) enables collaborative human-robot task execution through extended dialogue in order to 4) ensure robust execution of hierarchical tasks with complex constraints, such as sequential, non-ordering, and multiple paths of execution. The architecture ensures that the constraints are adhered to throughout the entire task execution, including during failures. The recovery of the architecture from issues during execution is validated by a human-robot team on a building task. 

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2. Learning of Complex-Structured Tasks from Verbal Instruction

   https://doi.org/10.1109/Humanoids43949.2019.9035032  

   Nicolescu, M; Arnold, N; Blankenburg, J; Feil-Seifer, D; Balajee Banisetty, S; Nicolescu, M; Palmer, A; Monteverde, T (October 2020, IEEERAS International Conference on Humanoid Robots)

   null (Ed.)

   This paper presents a novel approach to robot task learning from language-based instructions, which focuses on increasing the complexity of task representations that can be taught through verbal instruction. The major proposed contribution is the development of a framework for directly mapping a complex verbal instruction to an executable task representation, from a single training experience. The method can handle the following types of complexities: 1) instructions that use conjunctions to convey complex execution constraints (such as alternative paths of execution, sequential or nonordering constraints, as well as hierarchical representations) and 2) instructions that use prepositions and multiple adjectives to specify action/object parameters relevant for the task. Specific algorithms have been developed for handling conjunctions, adjectives and prepositions as well as for translating the parsed instructions into parameterized executable task representations. The paper describes validation experiments with a PR2 humanoid robot learning new tasks from verbal instruction, as well as an additional range of utterances that can be parsed into executable controllers by the proposed system. 

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3. Reactive Task Allocation and Planning of Quadrupedal and Wheeled Robots

   Zhou, Ziyi; Lee, Dong Jae; Yoshinaga, Yuki; Balakirsky, Stephen; Guo, Dejun; Zhao, Ye (January 2022, IEEE International Conference on Automation Science and Engineering CASE)

   This paper takes the first step towards a reactive, hierarchical multi-robot task allocation and planning framework given a global Linear Temporal Logic specification. The capabilities of both quadrupedal and wheeled robots are leveraged via a heterogeneous team to accomplish a variety of navigation and delivery tasks. However, when deployed in the real world, all robots can be susceptible to different types of disturbances, including but not limited to locomotion failures, human interventions, and obstructions from the environment. To address these disturbances, we propose task-level local and global reallocation strategies to efficiently generate updated action-state sequences online while guaranteeing the completion of the original task. These task reallocation approaches eliminate reconstructing the entire plan or resynthesizing a new task. To integrate the task planner with low-level inputs, a Behavior Tree execution layer monitors different types of disturbances and employs the reallocation methods to make corresponding recovery strategies. To evaluate this planning framework, dynamic simulations are conducted in a realistic hospital environment with a heterogeneous robot team consisting of quadrupeds and wheeled robots for delivery tasks. 

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4. Demand Characterization of CPS with Conditionally-Enabled Sensors

   https://doi.org/10.1109/RTCSA52859.2021.00025  

   Willcock, Aaron; Fisher, Nathan; Chantem, Thidapat (August 2021, International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA))

   Characterizing computational demand of Cyber-Physical Systems (CPS) is critical for guaranteeing that multiple hard real-time tasks may be scheduled on shared resources without missing deadlines. In a CPS involving repetition such as industrial automation systems found in chemical process control or robotic manufacturing, sensors and actuators used as part of the industrial process may be conditionally enabled (and disabled) as a sequence of repeated steps is executed. In robotic manufacturing, for example, these steps may be the movement of a robotic arm through some trajectories followed by activation of end-effector sensors and actuators at the end of each completed motion. The conditional enabling of sensors and actuators produces a sequence of Monotonically Ascending Execution times (MAE) with lower WCET when the sensors are disabled and higher WCET when enabled. Since these systems may have several predefined steps to follow before repeating the entire sequence each unique step may result in several consecutive sequences of MAE. The repetition of these unique sequences of MAE result in a repeating WCET sequence. In the absence of an efficient demand characterization technique for repeating WCET sequences composed of subsequences with monotonically increasing execution time, this work proposes a new task model to describe the behavior of real-world systems which generate large repeating WCET sequences with subsequences of monotonically increasing execution times. In comparison to the most applicable current model, the Generalized Multiframe model (GMF), an empirically and theoretically faster method for characterizing the demand is provided. The demand characterization algorithm is evaluated through a case study of a robotic arm and simulation of 10,000 randomly generated tasks where, on average, the proposed approach is 231 and 179 times faster than the state-of-the-art in the case study and simulation respectively. 

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5. Runtime Adaptive Task Inlining on Asynchronous Multitasking Runtime Systems

   https://doi.org/10.1145/3337821.3337915  

   Wagle, Bibek; Monil, Mohammad Alaul; Huck, Kevin; Malony, Allen D.; Serio, Adrian; Kaiser, Hartmut (January 2019, ICPP 2019 Proceedings of the 48th International Conference on Parallel Processing)

   As the era of high frequency, single core processors have come to a close, the new paradigm of many core processors has come to dominate. In response to these systems, asynchronous multitasking runtime systems have been developed as a promising solution to efficiently utilize these newly available hardware. Asynchronous multitasking runtime systems work by dividing a problem into a large number of fine grained tasks. However, as the number of tasks created increase, the overheads associated with task creation and management cannot be ignored. Task inlining, a method where the parent thread consumes a child thread, enables the runtime system to achieve the balance between parallelism and its overhead. As largely impacted by different processor architectures, the decision of task inlining is dynamic in nature. In this research, we present adaptive techniques for deciding, at runtime, whether a particular task should be inlined or not. We present two policies, a baseline policy that makes inlining decision based on a fixed threshold and an adaptive policy which decides the threshold dynamically at runtime. We also evaluate and justify the performance of these policies on different processor architectures. To the best of our knowledge, this is the first study of the impacts of adaptive policy at runtime for task inlining in an asynchronous multitasking runtime system on different processor architectures. From experimentation, we find that the baseline policy improves the execution time from 7.61% to 54.09%. Furthermore, the adaptive policy improves over the baseline policy by up to 74%. 

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