Recent studies have demonstrated that the dynamics of physical systems can be utilized for the desired information processing under the framework of physical reservoir computing (PRC). Robots with soft bodies are examples of such physical systems, and their nonlinear body-environment dynamics can be used to compute and generate the motor signals necessary for the control of their own behavior. In this simulation study, we extend this approach to control and embed not only one but also multiple behaviors into a type of soft robot called a tensegrity robot. The resulting system, consisting of the robot and the environment, is a multistable dynamical system that converges to different attractors from varying initial conditions. Furthermore, attractor analysis reveals that there exist "untrained attractors" in the state space of the system outside the training data. These untrained attractors reflect the intrinsic properties and structures of the tensegrity robot and its interactions with the environment. The impacts of these recent findings in PRC remain unexplored in embodied AI research. We here illustrate their potential to understand various features of embodied cognition that have not been fully addressed to date.

This is the second time Godzilla and King Kong have made a film appearance together in recent times with 2021's Godzilla vs Kong being the first instalment. Both films were directed by Adam Wingard. Godzilla x Kong made back its budget of 135m in the first weekend when it took in 195m at cinemas, according to figures from Box Office Mojo. In total, it has sold 209m in tickets so far and has scored a very respectable 92 percent Rotten Tomatoes audience rating. The origins of Godzilla go back 70 years to the first 1954 film release in Tokyo, Japan – Gojira, directed by Ishiro Honda.

The echo state property (ESP) represents a fundamental concept in the reservoir computing (RC) framework that ensures output-only training of reservoir networks by being agnostic to the initial states and far past inputs. However, the traditional definition of ESP does not describe possible non-stationary systems in which statistical properties evolve. To address this issue, we introduce two new categories of ESP: $\textit{non-stationary ESP}$, designed for potentially non-stationary systems, and $\textit{subspace/subset ESP}$, designed for systems whose subsystems have ESP. Following the definitions, we numerically demonstrate the correspondence between non-stationary ESP in the quantum reservoir computer (QRC) framework with typical Hamiltonian dynamics and input encoding methods using non-linear autoregressive moving-average (NARMA) tasks. We also confirm the correspondence by computing linear/non-linear memory capacities that quantify input-dependent components within reservoir states. Our study presents a new understanding of the practical design of QRC and other possibly non-stationary RC systems in which non-stationary systems and subsystems are exploited.

We provide high-speed implementations for simulating reservoirs described by $N$-coupled spin-torque oscillators. Here $N$ also corresponds to the number of reservoir nodes. We benchmark a variety of implementations based on CPU and GPU. Our new methods are at least 2.6 times quicker than the baseline for $N$ in range $1$ to $10^4$. More specifically, over all implementations the best factor is 78.9 for $N=1$ which decreases to 2.6 for $N=10^3$ and finally increases to 23.8 for $N=10^4$. GPU outperforms CPU significantly at $N=2500$. Our results show that GPU implementations should be tested for reservoir simulations. The implementations considered here can be used for any reservoir with evolution that can be approximated using an explicit method.

Abstract: Harnessing complex body dynamics has been a long-standing challenge in robotics. Soft body dynamics is a typical example of high complexity in interacting with the environment. An increasing number of studies have reported that these dynamics can be used as a computational resource. This includes the McKibben pneumatic artificial muscle, which is a typical soft actuator. This study demonstrated that various dynamics, including periodic and chaotic dynamics, could be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics that are not presented in training data could be embedded by using this capability of bifurcation embeddment. This implies that it is possible to embed various qualitatively different patterns into pneumatic artificial muscle by learning specific patterns, without the need to design and learn all patterns required for the purpose. Thus, this study sheds new light on a novel pathway to simplify the robotic devices and training of the control by reducing the external pattern generators and the amount and types of training data for the control. Main Text: INTRODUCTION Recent studies have revealed that mechanical devices can be designed to use their body dynamics for desired information processing, such as a mechanical random number generator (1) and mechanical neural networks (2). Furthermore, the natural dynamics of mechanical bodies not designed for computation can be used as an information processing resource. The complex dynamics in soft robotic arms, which are inspired by the octopus, can be used for real-time computation, embedding a timer, and controlling the arm by employing the approach of physical reservoir computing (PRC) (3-7).

Since the launch of OpenAI's GPT-4 API last month to beta testers, a loose group of developers has been experimenting with making agent-like ("agentic") implementations of the AI model that attempt to carry out multistep tasks with as little human intervention as possible. These homebrew scripts can loop, iterate, and spin-off new instances of an AI model as needed. Two experimental open source projects, in particular, have captured much attention on social media, especially among those who hype AI projects relentlessly: Auto-GPT, created by Toran Bruce Richards, and BabyAGI, created by Yohei Nakajima. They need a lot of human input and hand-holding along the way, so they're not yet as autonomous as promised. But they represent early steps toward more complex chaining AI models that could potentially be more capable than a single AI model working alone.

In order to operate in human environments, a robot's semantic perception has to overcome open-world challenges such as novel objects and domain gaps. Autonomous deployment to such environments therefore requires robots to update their knowledge and learn without supervision. We investigate how a robot can autonomously discover novel semantic classes and improve accuracy on known classes when exploring an unknown environment. To this end, we develop a general framework for mapping and clustering that we then use to generate a self-supervised learning signal to update a semantic segmentation model. In particular, we show how clustering parameters can be optimized during deployment and that fusion of multiple observation modalities improves novel object discovery compared to prior work. Models, data, and implementations can be found at github.com/hermannsblum/scim.

In my doctoral dissertation I investigate patterns appearing in sentences referring to the future. Such patterns are useful in predicting future events. I base the study on a multiple newspaper corpora. I firstly perform a preliminary study to find out that the patterns appearing in future-reference sentences often consist of disjointed elements within a sentence. Such patterns are also usually semantically and grammatically consistent, although lexically variant. Therefore, I propose a method for automatic extraction of such patterns, applying both grammatical (morphological) and semantic information to represent sentences in morphosemantic structure, and then extract frequent patterns, including those with disjointed elements. Next, I perform a series of experiments, in which I firstly train fourteen classifier versions and compare them to choose the best one. Next, I compare my method to the state-of-the-art, and verify the final performance of the method on a new dataset. I conclude that the proposed method is capable to automatically classify future-reference sentences, significantly outperforming state-of-the-art, and reaching 76% of F-score.

Internal motivations can prompt spontaneous changes in animal behavior. A recent study strives to design an artificial intelligence that can mimic animal-like actions using chaotic dynamics. A woman walking to a bus stop realizes that she forgot her keys; she suddenly turns around and runs home. Such spontaneous activities are hallmarks of animal behavior. Eager to capture the essence of the human brain, roboticists have tried to imitate these sorts of actions.