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ABSTRACT The transcriptional regulator PecS is encoded by select bacterial pathogens. For instance, in the plant pathogen Dickeya dadantii , PecS controls a range of virulence genes, including pectinase genes and the divergently oriented gene pecM , which encodes an efflux pump through which the antioxidant indigoidine is exported. In the plant pathogen Agrobacterium fabrum (formerly named Agrobacterium tumefaciens ), the pecS-pecM locus is conserved. Using a strain of A. fabrum in which pecS has been disrupted, we show here that PecS controls a range of phenotypes that are associated with bacterial fitness. PecS represses flagellar motility and chemotaxis, which are processes that are important for A. fabrum to reach plant wound sites. Biofilm formation and microaerobic survival are reduced in the pecS disruption strain, whereas the production of acyl homoserine lactone (AHL) and resistance to reactive oxygen species (ROS) are increased when pecS is disrupted. AHL production and resistance to ROS are expected to be particularly relevant in the host environment. We also show that PecS does not participate in the induction of vir genes. The inducing ligands for PecS, urate, and xanthine, may be found in the rhizosphere, and they accumulate within the plant host upon infection. Therefore, our data suggest that PecS mediates A. fabrum fitness during its transition from the rhizosphere to the host plant. IMPORTANCE PecS is a transcription factor that is conserved in several pathogenic bacteria, where it regulates virulence genes. The plant pathogen Agrobacterium fabrum is important not only for its induction of crown galls in susceptible plants but also for its role as a tool in the genetic manipulation of host plants. We show here that A. fabrum PecS controls a range of phenotypes, which would confer the bacteria an advantage while transitioning from the rhizosphere to the host plant. This includes the production of signaling molecules, which are critical for the propagation of the tumor-inducing plasmid. A more complete understanding of the infection process may inform approaches by which to treat infections as well as to facilitate the transformation of recalcitrant plant species. 

In this paper, we present a novel means of control design for probabilistic movement primitives (ProMPs). Our proposed approach makes use of control barrier functions and control Lyapunov functions defined by a ProMP distribution. Thus, a robot may move along a trajectory within the distribution while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. The control employs feedback linearization to handle nonlinearities in the system dynamics and real-time quadratic programming to ensure a solution exists that satisfies all safety constraints while minimizing control effort. Furthermore, we highlight how the proposed method may allow a designer to emphasize certain safety objectives that are more important than the others. A series of simulations and experiments demonstrate the efficacy of our approach and show it can run in real time. 

In this paper, we develop a novel and safe control design approach that takes demonstrations provided by a human teacher to enable a robot to accomplish complex manipulation scenarios in dynamic environments. First, an overall task is divided into multiple simpler subtasks that are more appropriate for learning and control objectives. Then, by collecting human demonstrations, the subtasks that require robot movement are modeled by probabilistic movement primitives (ProMPs). We also study two strategies for modifying the ProMPs to avoid collisions with environmental obstacles. Finally, we introduce a rule-base control technique by utilizing a finite-state machine along with a unique means of control design for ProMPs. For the ProMP controller, we propose control barrier and Lyapunov functions to guide the system along a trajectory within the distribution defined by a ProMP while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. This allows for better performance on nonlinear systems and offers solid stability and known bounds on the system state. A series of simulations and experimental studies demonstrate the efficacy of our approach and show that it can run in real time. Note to Practitioners —This paper is motivated by the need to create a teach-by-demonstration framework that captures the strengths of movement primitives and verifiable, safe control. We provide a framework that learns safe control laws from a probability distribution of robot trajectories through the use of advanced nonlinear control that incorporates safety constraints. Typically, such distributions are stochastic, making it difficult to offer any guarantees on safe operation. Our approach ensures that the distribution of allowed robot trajectories is within an envelope of safety and allows for robust operation of a robot. Furthermore, using our framework various probability distributions can be combined to represent complex scenarios in the environment. It will benefit practitioners by making it substantially easier to test and deploy accurate, efficient, and safe robots in complex real-world scenarios. The approach is currently limited to scenarios involving static obstacles, with dynamic obstacle avoidance an avenue of future effort.