Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipulation. To address this challenge, we design a novel AI-based framework that automates cable connector mating by integrating force control with deep visuotactile learning. Our system optimizes search-and-insertion strategies using first-order optimization over a multimodal transformer architecture trained on visual, tactile, and proprioceptive data. Additionally, we design a novel automated data collection and optimization pipeline that minimizes the need for machine learning expertise. The framework optimizes robot programs that run natively on standard industrial controllers, permitting human experts to audit and certify them. Experimental validations on a center console assembly task demonstrate significant improvements in cycle times and robustness compared to conventional robot programming approaches. Videos are available under https://claudius-kienle.github.io/AppMuTT.

Male fruit flies reared in a lab are more successful at mating after an encounter with a robotic dummy designed to look like a rival male. The finding could boost efforts to control populations of the flies, which are a major crop pest. The Mediterranean fruit fly (Ceratitis capitata) is one of the most destructive fruit pests in the world, found on every continent except Antarctica.

Many animals like to play, often for no other apparent reason than enjoyment. Pet owners know this is true for cats, dogs, even rodents--and scientists have observed the same in some fish, frogs, lizards, and birds. Are their minds and lives rich enough to make room for play? New research published in the journal Animal Behaviour suggests that bumblebees seem to enjoy rolling around wooden balls, without being trained or receiving rewards--presumably just because it's fun. "It shows that bees are not little robots that just respond to stimuli… and they do carry out activities that might be pleasurable," says lead author Samadi Galpayage, a researcher at the Queen Mary University of London.

Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered fragments, is fundamental to numerous applications, and yet most of the literature has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape with an arbitrary number of straight cuts, a generation model inspired by the celebrated Lazy caterer's sequence. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process. We define evaluation metrics and present experimental results to indicate that such puzzles are solvable completely automatically.

In the __init__ function, we set up the network. The parameter dimensions is a list of layer dimensions, where the first is the width of the input, the last is the width of the output, and all others are hidden dimensions. The __init__ function iterates through these n dimensions to create n-1 weight matrices using Glorot Normal initialization, which are stored as layers. If bias is enabled, a non-zero bias vector is also stored for each layer. The model uses ReLU activation for all internal layers.

Optimizing a neural network's performance is a tedious and time taking process, this iterative process does not have any defined solution which can work for all the problems. Optimization can be roughly categorized into - Architecture and Hyperparameter optimization. Many algorithms have been devised to address this problem. In this paper we introduce a neural network architecture (Deep Genetic Network) which will optimize its parameters during training based on its fitness. Deep Genetic Net uses genetic algorithms along with deep neural networks to address the hyperparameter optimization problem, this approach uses ideas like mating and mutation which are key to genetic algorithms which help the neural net architecture to learn to optimize its hyperparameters by itself rather than depending on a person to explicitly set the values. Using genetic algorithms for this problem proved to work exceptionally well when given enough time to train the network. The proposed architecture is found to work well in optimizing hyperparameters in affine, convolutional and recurrent layers proving to be a good choice for conventional supervised learning tasks.

Two species of'promiscuous' monkeys in Tanzania's Gombe National Park have likely been interbreeding for hundreds or even thousands of years, according to a new study. Using DNA extracted from the feces of 144 guenon monkeys in the park, researchers have documented evidence of ongoing mating between two genetically distinct groups. Guenon monkeys are known for their flashy colors and striking facial features, including large noses and bushy beards, which experts have long thought to be species-specific signals used for mate selection. But, the new findings suggest they might not be so picky after all. Roughly 15 percent of the population consists of hybrids, which are identified by their combined markings from both parental species.

Watch: This may be the first footage of a blue whale "heat run." Blue whales are the largest animals on Earth, but we know surprisingly little about their complex social interactions--and they've rarely been recorded on camera. But new footage filmed off the coast of Sri Lanka by pro whale photographer Patrick Dykstra, in conjunction with blue whale scientist Howard Martenstyn, may be a first. Their video shows what they believe is the first known clip of a blue whale "heat run." Heat runs have been well documented in humpback whales, but no known footage exists of the behavior in blue whales (or at least that Dykstra or National Geographic could find).

Watch: This may be the first footage of a blue whale "heat run." Blue whales are the largest animals on Earth, but we know surprisingly little about their complex social interactions--and they've rarely been recorded on camera. But new footage filmed off the coast of Sri Lanka by pro whale photographer Patrick Dykstra, in conjunction with blue whale scientist Howard Martenstyn, may be a first. Their video shows what they believe is the first known clip of a blue whale "heat run." Heat runs have been well documented in humpback whales, but no known footage exists of the behavior in blue whales (or at least that Dykstra or National Geographic could find).

Understanding the evolution of human society, as a complex adaptive system, is a task that has been looked upon from various angles. In this paper, we simulate an agent-based model with a high enough population tractably. To do this, we characterize an entity called \textit{society}, which helps us reduce the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We propose a very realistic setting, where we design a joint alternate maximization step algorithm to maximize a certain \textit{fitness} function, which we believe simulates the way societies develop. Our key contributions include (i) proposing a novel protocol for simulating the evolution of a society with cheap, non-optimal joint alternate maximization steps (ii) providing a framework for carrying out experiments that adhere to this joint-optimization simulation framework (iii) carrying out experiments to show that it makes sense empirically (iv) providing an alternate justification for the use of \textit{society} in the simulations.