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1. Dynamic Control of Probabilistic Simple Temporal Networks

   https://doi.org/10.1609/aaai.v34i06.6538  

   Gao, Michael; Popowski, Lindsay; Boerkoel, Jim (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   The controllability of a temporal network is defined as an agent's ability to navigate around the uncertainty in its schedule and is well-studied for certain networks of temporal constraints. However, many interesting real-world problems can be better represented as Probabilistic Simple Temporal Networks (PSTNs) in which the uncertain durations are represented using potentially-unbounded probability density functions. This can make it inherently impossible to control for all eventualities. In this paper, we propose two new dynamic controllability algorithms that attempt to maximize the likelihood of successfully executing a schedule within a PSTN. The first approach, which we call Min-Loss DC, finds a dynamic scheduling strategy that minimizes loss of control by using a conflict-directed search to decide where to sacrifice the control in a way that optimizes overall success. The second approach, which we call Max-Gain DC, works in the other direction: it finds a dynamically controllable schedule and then attempts to progressively strengthen it by capturing additional uncertainty. Our approaches are the first known that work by finding maximally dynamically controllable schedules. We empirically compare our approaches against two existing PSTN offline dispatch approaches and one online approach and show that our Min-Loss DC algorithm outperforms the others in terms of maximizing execution success while maintaining competitive runtimes. 

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2. Simple Temporal Networks for Improvisational Teamwork

   https://doi.org/10.1609/icaps.v32i1.19809  

   Morgan, Malia; Schalkwyk, Julianna; Wang, Huaxiaoyue; Davalos, Hannah; Martinez, Ryan; Rohilla, Vibha; Boerkoel, James (June 2022, Proceedings of the International Conference on Automated Planning and Scheduling)

   When communication between teammates is limited to observations of each other's actions, agents may need to improvise to stay coordinated. Unfortunately, current methods inadequately capture the uncertainty introduced by a lack of direct communication. This paper augments existing frameworks to introduce Simple Temporal Networks for Improvisational Teamwork (STN-IT)—a formulation that captures both the temporal dependencies and uncertainties between agents who need to coordinate but lack reliable communication. We define the notion of strong controllability for STN-ITs, which establishes a static scheduling strategy for controllable agents that produces a consistent team schedule, as long as non-communicative teammates act within known problem constraints. We provide both an exact and approximate approach for finding strongly controllable schedules, empirically demonstrate the trade-offs between these approaches on benchmarks of STN-ITs, and show analytically that the exact method is correct. 

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3. Simple Temporal Networks for Improvisational Teamwork

   Morgan, M.; Schalkwyk, J.; Wang, H.; Davalos, H.; Martinez, R.; Rohilla, V.; Boerkoel, J. (March 2022, AAAI Spring Symposium Series on Can We Talk? How to Design Multi-Agent Systems In the Absence of Reliable Communications)

   When communication between teammates is limited to observations of each other’s actions, agents may need to improvise to stay coordinated. Unfortunately, current methods inadequately capture the uncertainty introduced by a lack of direct communication. This paper augments existing frameworks to introduce Simple Temporal Networks for Improvisational Teamwork (STN-IT) — a formulation that captures both the temporal dependencies and uncertainties between agents who need to coordinate, but lack reliable communication. We define the notion of strong controllability for STN-ITs, which establishes a static scheduling strategy for controllable agents that produces a consistent team schedule, as long as non-communicative teammates act within known problem constraints. We provide both an exact and approximate approach for finding strongly controllable schedules, empirically demonstrate the trade-offs between these two approaches on a benchmark of STN-ITs, and show analytically that the exact method is correct. In addition, we provide an empirical analysis of the exact and approximate approaches’ efficiency 

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4. Sparse Linear Ensemble Systems and Structural Controllability

   https://doi.org/10.1109/TAC.2021.3097289  

   Chen, Xudong (July 2021, IEEE Transactions on Automatic Control)

   The paper introduces and solves a structural controllability problem for continuum ensembles of linear time-invariant systems. All the individual linear systems of an ensemble are sparse, governed by the same sparsity pattern. Controllability of an ensemble system is, by convention, the capability of using a common control input to simultaneously steer every individual systems in it. A sparsity pattern is structurally controllable if it admits a controllable linear ensemble system. A main contribution of the paper is to provide a graphical condition that is necessary and sufficient for a sparsity pattern to be structurally controllable. Like other structural problems, the property of being structural controllable is monotone. We provide a complete characterization of minimal sparsity patterns as well. 

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5. AFREEs: Active Fiber Reinforced Elastomeric Enclosures

   https://doi.org/10.1109/RoboSoft48309.2020.9115988  

   Yoshida, Kyle T.; Ren, Xinyi; Blumenschein, Laura H.; Okamura, Allison M.; Luo, Ming (May 2020, IEEE International Conference on Soft Robotics (RoboSoft))

   Soft continuum manipulators provide a safe alternative to traditional rigid manipulators, because their bodies can absorb and distribute contact forces. Soft manipulators have near infinite potential degrees of freedom, but a limited number of control inputs. This underactuation means soft continuum manipulators often lack either the controllability or the dexterity to achieve desired tasks. In this work, we present an extension of McKibben actuators, which have well-known models, that increases the controllable degrees of freedom using active reconfiguration of the constraining fibers. These Active Fiber Reinforced Elastomeric Enclosures (AFREEs) preform some combination of length change and twisting, depending on the fiber configuration. Experimental results shows that by changing the fiber angles within a range of -30 to 30 degrees and actuating the resulting configuration between 10.3 kPa and 24.1 kPa, we can achieve twists between ± 60 degrees and displacements between -2 and 4 mm. By additionally controlling the fiber lengths and pressure, we can modify the AFREE kinematics further, creating dynamic behaviors and trajectories of actuation. The presented actuator creates the possibility to reconFigure actuator kinematics to meet desired soft robot motions. 

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