--Salps are marine animals consisting of chains of jellyfish-like units. Their capacity for effective underwater undulatory locomotion through coordinating multi-jet propulsion has aroused significant interest in the field of robotics and inspired extensive research including design, modeling, and control. In this paper, we conduct a comprehensive analysis of the locomotion of salp-like systems using the robotic platform "LandSalp" based on geometric mechanics, including mechanism design, dynamic modeling, system identification, and motion planning and control. Our work takes a step toward a better understanding of salps' underwater locomotion and provides a clear path for extending these insights to more complex and capable underwater robotic systems. Furthermore, this study illustrates the effectiveness of geometric mechanics in bio-inspired robots for efficient data-driven locomotion modeling, demonstrated by learning the dynamics of LandSalp from only 3 minutes of experimental data. Lastly, we extend the geometric mechanics principles to multi-jet propulsion systems with stability considerations and validate the theory through experiments on the LandSalp hardware. These creatures are capable of efficient underwater undulatory locomotion by coordinating multi-jet propulsion. The structure and locomotion patterns of salps are closely related, which has attracted widespread interest in both biological and ecological research [1-5]. In the field of robotics, salps have attracted increasing attention due to their jet propulsion by expelling water through contraction, efficient underwater locomotion, and multi-unit coordination. Salps and jellyfish have inspired numerous robotic studies on the design of jet propulsion soft robots [6-12] and multi-robot coordination [13-17]. However, in the field of motion planning and control, most studies primarily consider undulatory locomotion by self-propulsion via body deformation [18-23], with only a few works involving underwater locomotion using jet propulsion [24-26]. This work was supported in part by ONR A ward N00014-23-1-2171. All the authors are with the Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University, Corvallis, OR USA. The units composing biological salps are called "zooids" (i.e., pseudoan-imals or not-quite-animals) because they exhibit many properties of animals but are not independent organisms from the colony. To discuss the general properties of multi-jet locomotion without making claims about the biological systems that inspire them, we use the terminology "chains" and "units" throughout this paper. The salp picture is reproduced from [27].

The challenge of generalizing causal knowledge across different environments is pervasive in scientific explorations, including in AI, ML, and Data Science. Experiments are usually performed in one environment (e.g., in a lab, on Earth) with the intent, almost invariably, of being used elsewhere (e.g., outside the lab, on Mars), where the conditions are likely to be different. In the causal inference literature, this generalization task has been formalized under the rubric of transportability (Pearl and Bareinboim, 2011), where a number of criteria and algorithms have been developed for various settings. Despite the generality of such results, transportability theory has been confined to atomic, do()-interventions. In practice, many real-world applications require more complex, stochastic interventions; for instance, in reinforcement learning, agents need to continuously adapt to the changing conditions of an uncertain and unknown environment. In this paper, we extend transportability theory to encompass these more complex types of interventions, which are known as "soft," both relative to the input as well as the target distribution of the analysis. Specifically, we develop a graphical condition that is both necessary and sufficient for deciding soft-transportability. Second, we develop an algorithm to determine whether a non-atomic intervention is computable from a combination of the distributions available across domains. As a corollary, we show that the σ-calculus is complete for the task of soft-transportability.

In this paper, we address the problems of identifying linear structural equation models and discovering the constraints they imply. We first extend the half-trek criterion to cover a broader class of models and apply our extension to finding testable constraints implied by the model. We then show that any semi-Markovian linear model can be recursively decomposed into simpler sub-models, resulting in improved identification and constraint discovery power. Finally, we show that, unlike the existing methods developed for linear models, the resulting method subsumes the identification and constraint discovery algorithms for non-parametric models.

Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In this paper, we demonstrate that it is possible to learn accurate models directly from data with missing values for certain training data and target models. We propose a unified approach for checking the necessity of data imputation to learn accurate models across various widely-used machine learning paradigms. We build efficient algorithms with theoretical guarantees to check this necessity and return accurate models in cases where imputation is unnecessary. Our extensive experiments indicate that our proposed algorithms significantly reduce the amount of time and effort needed for data imputation without imposing considerable computational overhead.

Kurt "The CyberGuy" Knutsson introduces Somatic's AI janitor robot that was created to help with cleaning restrooms. Oregon State University is warning students to "avoid all robots" and to "not open" any food delivery robots due to an ongoing bomb threat on the campus. On Tuesday afternoon, Oregon State University (OSU) issued an alert to students at the Corvallis, Oregon, university that there was a bomb threat related to the Starship food delivery robots. Oregon State University told students to avoid Starship food delivery robots due to a bomb threat. OSU advised people not open the robots and to avoid them "until further notice."

Abstract-- We present a novel general model that unifies the kinematics of constant curvature and constant twist continuum manipulators. Combining this kinematics with energy-based physics, we derive a linear mapping from actuator configuration to manipulator deformation that is analogous to traditional robot forward kinematics. The combination of generality and linearity makes the model useful for control and planning algorithms. Finally, our model is shown to be accurate through experimental validation on manipulators with pneumatic artificial muscles. I. INTRODUCTION While the motion of traditional robots comes from their discrete joints, a continuum manipulator moves by deforming along its entire arc. These manipulators are often composed of rigid skeletons and soft actuators.

CORVALLIS, Ore. – Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 meters by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The 100-meter record builds on earlier achievements by the robot, including traversing 5 kilometers in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. Cassie was developed under the direction of Oregon State robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Defense Advanced Research Projects Agency, or DARPA.

CORVALLIS, Ore. – Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 meters by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The run can also be seen on YouTube.) The 100-meter record builds on earlier achievements by the robot, including traversing 5 kilometers in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge.

CORVALLIS, Ore. – A new study by the Oregon State University College of Engineering shows that machine learning techniques can offer powerful new tools for advancing personalized medicine, care that optimizes outcomes for individual patients based on unique aspects of their biology and disease features. The research with machine learning, a branch of artificial intelligence in which computer systems use algorithms and statistical models to look for trends in data, tackles long-unsolvable problems in biological systems at the cellular level, said Oregon State's Brian D. Wood, who conducted the study with then OSU Ph.D. student Ehsan Taghizadeh and Helen M. Byrne of the University of Oxford. "Those systems tend to have high complexity – first because of the vast number of individual cells and second, because of the highly nonlinear way in which cells can behave," said Wood, a professor of environmental engineering. "Nonlinear systems present a challenge for upscaling methods, which is the primary means by which researchers can accurately model biological systems at the larger scales that are often the most relevant." A linear system in science or mathematics means any change to the system's input results in a proportional change to the output; a linear equation, for example, might describe a slope that gains 2 feet vertically for every foot of horizontal distance.

CORVALLIS, Ore. – Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometers, completing the route in just over 53 minutes. Cassie was developed under the direction of robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Defense Advanced Research Projects Agency, or DARPA. Since Cassie's introduction in 2017, in collaboration with artificial intelligence professor Alan Fern OSU students funded by the National Science Foundation and the DARPA Machine Common Sense program have been exploring machine learning options for the robot. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. "The Dynamic Robotics Laboratory students in the OSU College of Engineering combined expertise from biomechanics and existing robot control approaches with new machine learning tools," said Hurst, who co-founded Agility in 2017.