Dataset achieve significant performance improvements in both closed-loop and open-loop metrics.

An open research question in robotics is how to combine the benefits of model-free reinforcement learning (RL) - known for its strong task performance and flexibility in optimizing general reward formulations - with the robustness and online replanning capabilities of model predictive control (MPC). This paper provides an answer by introducing a new framework called Actor-Critic Model Predictive Control. The key idea is to embed a differentiable MPC within an actor-critic RL framework. The proposed approach leverages the short-term predictive optimization capabilities of MPC with the exploratory and end-to-end training properties of RL. The resulting policy effectively manages both short-term decisions through the MPC-based actor and long-term prediction via the critic network, unifying the benefits of both model-based control and end-to-end learning. We validate our method in both simulation and the real world with a quadcopter platform across various high-level tasks. We show that the proposed architecture can achieve real-time control performance, learn complex behaviors via trial and error, and retain the robustness inherent to MPC.

MADISON, Wis., April 17, 2023 (SEND2PRESS NEWSWIRE) -- David P. Perrodin, Ph.D., collaborates with renowned actor Ben Hauck and audio streaming company Spotify to create the audio version of his book "The Velocity of Information – Human Thinking During Chaotic Times." Utilizing personal interviews and explorations of historical and contemporary events, "The Velocity of Information" gives you an expectation of how both individuals and the masses will behave during different stages of chaos. "This book provides twelve strategies to become more resilient to burnout and increase your survival chances during uncertain times," explains Dr. Perrodin. Released in 2022 in both print and eBook formats, Rowman & Littlefield published The Velocity of Information, which is now featured in distinguished libraries across the globe, including: The Library of Congress, Bavarian State Library in Munich, and The Chinese University of Hong Kong Library. Professional actor and narrator Ben Hauck jumped at the opportunity to narrate the audiobook.

To be useful tools in real scenarios, humanoid robots must realize tasks dynamically. This means that they must be capable of applying substantial forces, rapidly swinging their limbs, and also mitigating impacts that may occur during the motion. Towards creating capable humanoids, this letter presents the leg of the robot TELLO and demonstrates how it embodies two new fundamental design concepts for dynamic legged robots. The limbs follows the principles of: (i) Cooperative Actuation (CA), by combining motors in differential configurations to increase the force capability of the limb. We demonstrate that the CA configuration requires half the motor torque to perform a jump in comparison to conventional serial design configurations. And (ii) proximal actuation, by placing heavy motors near the body to reduce the inertia of the limb. To quantify the effect of motor placement on the robot's dynamics, we introduce a novel metric entitle Centroidal Inertia Isotropy (CII). We show that the design of state-of-the-art dynamic legged robots empirically increase the CII to improve agility and facilitate model-based control. We hope this metric will enable a quantifiable way to design these machines in the future.

But a team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable. Then, on the fly, it generates control signals for a camera-equipped autonomous drone, which preserve that framing as the actors move. With our solution, if the subject turns 180 degrees, our drones are able to circle around and keep focus on the face. The researchers tested the system at CSAIL's motion-capture studio, using a quadrotor (four-propeller) drone.

In many situations, Miniature Aerial Vehicles (MAVs) are limited to using only on-board sensors for navigation. This limits the data available to algorithms used for stabilization and localization, and current control methods are often insufficient to allow reliable hovering in place or trajectory following. In this research, we explore using machine learning to predict the drift (flight path errors) of an MAV while executing a desired flight path. This predicted drift will allow the MAV to adjust it's flightpath to maintain a desired course.

The propagation of blood flow along the fetoplacental arterial system has been hypothesized to have a compensatory response to placental anomalies that may result in fetal stress. When the placenta generates increased resistance, the umbilical artery blood flow would decrease and in the worst scenario become absent, which will lead to fetal asphyxia and hypoxia. To compensate for the decreased oxygen supply from maternal placenta, the fetal middle cerebral arteries would become dilated leading to an increased diastolic flow, hence more oxygen. This compensatory phase , however, only lasts for a certain period of time, after which the hypoxia may lead to fetal demise or long term irreversible organ damages. In high-risk pregnancies, Doppler ultrasound technology is commonly used to monitor the fetoplacental arterial blood flow to assess fetal well being. If the anomalies occur prior to the end of the 40-week of gestation, surgical or aggressive medical intervention might be necessary to save the fetal life. Timing this intervention, however, is complex due to the fine balancing act to minimize potential risks from prematurity and organ damage vs. rescuing a fetal life through cesarean section or aggressive medical treatment or natural delivery at the earliest possible gestational age. A reasonable goal is to allow the pregnancy to continue to the point just before fetal damage occurs. To achieve that goal, various testing criteria, e.g. venous Doppler and fetal heart rate, have been used to identify de-compensation. In this work, we conducted computer simulation of the fetoplacental arterial blood flow of a Systemic Lupus Erythematosus (SLE) pregnancy based on Doppler blood flow readings taken during the 10-day period prior to the delivery. The simulation suggests that timing the delivery based on either Doppler waveform readings or fetal heart rates give similar pregnancy outcome.