In a multi-modal system which combines thruster and legged locomotion such our state-of-the-art Harpy platform to perform dynamic locomotion. Therefore, it is very important to have a proper estimate of Thruster force. Harpy is a bipedal robot capable of legged-aerial locomotion using its legs and thrusters attached to its main frame. we can characterize thruster force using a thrust stand but it generally does not account for working conditions such as battery voltage. In this study, we present a momentum-based thruster force estimator. One of the key information required to estimate is terrain information. we show estimation results with and without terrain knowledge. In this work, we derive a conjugate momentum thruster force estimator and implement it on a numerical simulator that uses thruster force to perform thruster-assisted walking.

Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly seen in nature, such as Chukar birds using their wings to run on a nearly vertical wall. In this work, we developed a capture-point-based controller integrated with a quadratic programming (QP) solver which is used to create a thruster-assisted dynamic bipedal walking controller for our state-of-the-art Harpy platform. Harpy is a bipedal robot capable of legged-aerial locomotion using its legs and thrusters attached to its main frame. While capture point control based on centroidal models for bipedal systems has been extensively studied, the use of these thrusters in determining the capture point for a bipedal robot has not been extensively explored. The addition of these external thrust forces can lead to interesting interpretations of locomotion, such as virtual buoyancy studied in aquatic-legged locomotion. In this work, we derive a thruster-assisted bipedal walking with the capture point controller and implement it in simulation to study its performance.

Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly seen in nature, such as Chukar birds using their wings to run on a nearly vertical wall. In this work, we show quadratic programming with contact constraints which is then given to the whole body controller to map on robot states to produce a thruster-assisted slope walking controller for our state-of-the-art Harpy platform. Harpy is a bipedal robot capable of legged-aerial locomotion using its legs and thrusters attached to its main frame. The optimization-based walking controller has been used for dynamic locomotion such as slope walking, but the addition of thrusters to perform inclined slope walking has not been extensively explored. In this work, we derive a thruster-assisted bipedal walking with the quadratic programming (QP) controller and implement it in simulation to study its performance.

Despite major advancements in control design that are robust to unplanned disturbances, bipedal robots are still susceptible to falling over and struggle to negotiate rough terrains. By utilizing thrusters in our bipedal robot, we can perform additional posture manipulation and expand the modes of locomotion to enhance the robot's stability and ability to negotiate rough and difficult-to-navigate terrains. In this paper, we present our efforts in designing a controller based on capture point control for our thruster-assisted walking model named Harpy and explore its control design possibilities. While capture point control based on centroidal models for bipedal systems has been extensively studied, the incorporation of external forces that can influence the dynamics of linear inverted pendulum models, often used in capture point-based works, has not been explored before. The inclusion of these external forces can lead to interesting interpretations of locomotion, such as virtual buoyancy studied in aquatic-legged locomotion. This paper outlines the dynamical model of our robot, the capture point method we use to assist the upper body stabilization, and the simulation work done to show the controller's feasibility.

Four bedraggled adventurers stand together on the shore of a desolate island, shivering in the evening mist. They don't know each other, and their motives for being here are unclear. But as they make stilted conversation they see, emerging from the briny waters, figures dressed in the rags of sailor outfits, moaning and shuffling and horrible. The adventurers stand around, roll some dice and chat some more, as the undead seamen lurch ever closer. Looking on at this desperate scene, I think to myself, "What the hell? Why can't anyone make a decision? We've been here for half an hour! We've not even begun the proper adventure yet!" Dungeons and Dragons has always been there in the background of my life.

The Silicon Valley-military industrial complex is increasingly in the crosshairs of artificial intelligence engineers. A few weeks ago, Google was reported to be backing out of a Pentagon contract around Project Maven, which would use image recognition to automatically evaluate photos. Earlier this year, AI researchers around the world joined petitions calling for a boycott of any research that could be used in autonomous warfare. For Paul Scharre, though, such petitions barely touch the deep complexity, nuance, and ambiguity that will make evaluating autonomous weapons a major concern for defense planners this century. In Army of None, Scharre argues that the challenges around just the definitions of these machines will take enormous effort to work out between nations, let alone handling their effects.

In contemporary sci-fi--HBO's "Westworld," for example--sentient machines take up arms against humanity. In the real world, intelligent--and increasingly autonomous--robots are being created with weapons already in hand. More than 16 countries (not to mention terrorist groups like the Islamic State) already possess armed drones. Militaries around the globe are racing to deploy robots at sea, on the ground and in the air. For now, these machines operate mostly under human control, but that may not be the case for long.

With the release of Apple's Siri and comparable voice search assistance from Microsoft and Google, you might have speculated why it took so long for speech recognition innovation to progress to this stage. In addition, one may also wonder what the future holds for natural language-based machine intelligence learning and its impact on our everyday lives. A closer look at the history and development of voice recognition technology may be somewhat akin to watching a toddler grow up, advancing from the baby-talk level and developing terminologies of countless words to responding to queries with fast, amusing repartees, just like what the clever digital assistant Siri does. Here is a close depiction at the innovations of the past generations with regards to speech recognition and what the future has in store for this technology. The "Audrey" system is the earliest speech recognition device that could recognize only digits.

That's not because it's impossible to ban weapons technologies. Some 192 nations have signed the Chemical Weapons Convention that bans chemical weapons, for example. But it hasn't suggested it would be open to international agreement banning autonomous weapons. In 2015, the UK government responded to calls for a ban on autonomous weapons by saying there was no need for one, and that existing international law was sufficient.

"Almost all the basic methods used by