Being widespread in human motor behavior, dynamic movements demonstrate higher efficiency and greater capacity to address a broader range of skill domains compared to their quasi-static counterparts. Among the frequently studied dynamic manipulation problems, robotic juggling tasks stand out due to their inherent ability to scale their difficulty levels to arbitrary extents, making them an excellent subject for investigation. In this study, we explore juggling patterns with mixed throw heights, following the vanilla siteswap juggling notation, which jugglers widely adopted to describe toss juggling patterns. This requires extending our previous analysis of the simpler cascade juggling task by a throw-height sequence planner and further constraints on the end effector trajectory. These are not necessary for cascade patterns but are vital to achieving patterns with mixed throw heights. Using a simulated environment, we demonstrate successful juggling of most common 3-9 ball siteswap patterns up to 9 ball height, transitions between these patterns, and random sequences covering all possible vanilla siteswap patterns with throws between 2 and 9 ball height. https://kai-ploeger.com/beyond-cascades

Ringling Bros. circus star who performs by the name Nick Nack walks Fox News Digital through the process of learning a balancing act called rola bola, which he combines with many other talents, like juggling and comedy. Jan Damm performs as Nick Nack in the Ringling Bros. and Barnum & Bailey circus show. His character has a large comedic presence, but he also has other tricks up his sleeve. Damm performs a balancing act called rola bola and is a master juggler. He is also joined on stage by a unique partner, a robotic dog named Bailey.

Dynamic movements are ubiquitous in human motor behavior as they tend to be more efficient and can solve a broader range of skill domains than their quasi-static counterparts. For decades, robotic juggling tasks have been among the most frequently studied dynamic manipulation problems since the required dynamic dexterity can be scaled to arbitrarily high difficulty. However, successful approaches have been limited to basic juggling skills, indicating a lack of understanding of the required constraints for dexterous toss juggling. We present a detailed analysis of the toss juggling task, identifying the key challenges and formalizing it as a trajectory optimization problem. Building on our state-of-the-art, real-world toss juggling platform, we reach the theoretical limits of toss juggling in simulation, evaluate a resulting real-time controller in environments of varying difficulty and achieve robust toss juggling of up to 17 balls on two anthropomorphic manipulators.

A new artificial intelligence system (AI) could watch and listen to your videos and label things that are happening. MIT researchers have developed a technique that teaches AI to capture actions shared between video and audio. For example, their method can understand that the act of a baby crying in a video is related to the spoken word "crying" in a sound clip. It's part of an effort to teach AI how to understand concepts that humans have no trouble learning, but that computers find hard to grasp. "The prevalent learning paradigm, supervised learning, works well when you have datasets that are well described and complete," AI expert Phil Winder told Lifewire in an email interview.

Humans observe the world through a combination of different modalities, like vision, hearing, and our understanding of language. Machines, on the other hand, interpret the world through data that algorithms can process. So, when a machine "sees" a photo, it must encode that photo into data it can use to perform a task like image classification. This process becomes more complicated when inputs come in multiple formats, like videos, audio clips, and images. "The main challenge here is, how can a machine align those different modalities? As humans, this is easy for us. We see a car and then hear the sound of a car driving by, and we know these are the same thing. But for machine learning, it is not that straightforward," says Alexander Liu, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and first author of a paper tackling this problem.

Robots that can learn in the physical world will be important to en-able robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as juggling, learning in the real-world is particularly challenging as one must push the limits of the robot and its actuation without harming the system, amplifying the necessity of sample efficiency and safety for robot learning algorithms. In contrast to prior work which mainly focuses on the learning algorithm, we propose a learning system, that directly incorporates these requirements in the design of the policy representation, initialization, and optimization. We demonstrate that this system enables the high-speed Barrett WAM manipulator to learn juggling two balls from 56 minutes of experience with a binary reward signal. The final policy juggles continuously for up to 33 minutes or about 4500 repeated catches. The videos documenting the learning process and the evaluation can be found at https://sites.google.com/view/jugglingbot

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. At the Queensland University of Technology, in Australia, roboticists have spent the last 14 years honing a robot navigation system modeled on the brains of rats. This biologically inspired approach, they hope, could help robots navigate dynamic environments without requiring advanced, costly sensors and computationally intensive algorithms.