In this work, we present a novel control approach based on partial feedback linearization (PFL) for the stabilization of a suspended aerial platform with an attached load. Such systems are envisioned for various applications in construction sites involving cranes, such as the holding and transportation of heavy objects. Our proposed control approach considers the underactuation of the whole system while utilizing its coupled dynamics for stabilization. We demonstrate using numerical stability analysis that these coupled terms are crucial for the stabilization of the complete system. We also carried out robustness analysis of the proposed approach in the presence of external wind disturbances, sensor noise, and uncertainties in system dynamics. As our envisioned target application involves cranes in outdoor construction sites, our control approaches rely on only onboard sensors, thus making it suitable for such applications. We carried out extensive simulation studies and experimental tests to validate our proposed control approach.

Explicit information seeking is essential to human problem-solving in practical environments characterized by incomplete information and noisy dynamics. When the true environmental state is not directly observable, humans seek information to update their internal dynamics and inform future decision-making. Although existing Large Language Model (LLM) planning agents have addressed observational uncertainty, they often overlook discrepancies between their internal dynamics and the actual environment. We introduce Information Seeking Decision Planner (InfoSeeker), an LLM decision-making framework that integrates task-oriented planning with information seeking to align internal dynamics and make optimal decisions under uncertainty in both agent observations and environmental dynamics. InfoSeeker prompts an LLM to actively gather information by planning actions to validate its understanding, detect environmental changes, or test hypotheses before generating or revising task-oriented plans. To evaluate InfoSeeker, we introduce a novel benchmark suite featuring partially observable environments with incomplete observations and uncertain dynamics. Experiments demonstrate that InfoSeeker achieves a 74% absolute performance gain over prior methods without sacrificing sample efficiency. Moreover, InfoSeeker generalizes across LLMs and outperforms baselines on established benchmarks such as robotic manipulation and web navigation. These findings underscore the importance of tightly integrating planning and information seeking for robust behavior in partially observable environments. The project page is available at https://infoseekerllm.github.io

Compared to conventional robots, flexible manipulators offer many advantages, such as faster end-effector velocities and less energy consumption. However, their flexible structure can lead to undesired oscillations. Therefore, the applied control strategy should account for these elasticities. A feedforward controller based on an inverse model of the system is an efficient way to improve the performance. However, unstable internal dynamics arise for many common flexible robots and stable inversion must be applied. In this contribution, an approximation of the original stable inversion approach is proposed. The approximation simplifies the problem setup, since the internal dynamics do not need to be derived explicitly for the definition of the boundary conditions. From a practical point of view, this makes the method applicable to more complex systems with many unactuated degrees of freedom. Flexible manipulators modeled by the absolute nodal coordinate formulation (ANCF) are considered as an application example.

So, say if we want to predict the future, rather than using the whole history, we can use the Markov State. The Markov State essentially contains no less information than the history. So, here the probability of getting to a future state St 1 given state St is the same as getting to St 1 given all the previous states. This is because the state St already contains the information about the previous states embedded in it. Say, we have a game in which there is a waiter at a restaurant.

AI strategy: banks need to have a clear vision on what AI is to achieve; how they want to integrate it within their organization; feasibility and impact of investments and possible consequences on their internal dynamics. Data management: invest in the creation and storage of a large amounts of data to train the AI algorithms. Dividends yielded by AI are related to the quality and the quantity of the data recorded or stored. Internal digitization: digitize processes and operations, promote a pro-technology culture, and familiarize their employees with emerging technologies. It is important to educate them AI will complement and enhance their work and not replace them.

We investigate how neural networks can learn and process languages with hierarchical, compositional semantics. To this end, we define the artificial task of processing nested arithmetic expressions, and study whether different types of neural networks can learn to compute their meaning. We find that recursive neural networks can implement a generalising solution to this problem, and we visualise this solution by breaking it up in three steps: project, sum and squash. As a next step, we investigate recurrent neural networks, and show that a gated recurrent unit, that processes its input incrementally, also performs very well on this task: the network learns to predict the outcome of the arithmetic expressions with high accuracy, although performance deteriorates somewhat with increasing length. To develop an understanding of what the recurrent network encodes, visualisation techniques alone do not suffice. Therefore, we develop an approach where we formulate and test multiple hypotheses on the information encoded and processed by the network. For each hypothesis, we derive predictions about features of the hidden state representations at each time step, and train 'diagnostic classifiers' to test those predictions. Our results indicate that the networks follow a strategy similar to our hypothesised 'cumulative strategy', which explains the high accuracy of the network on novel expressions, the generalisation to longer expressions than seen in training, and the mild deterioration with increasing length. This in turn shows that diagnostic classifiers can be a useful technique for opening up the black box of neural networks. We argue that diagnostic classification, unlike most visualisation techniques, does scale up from small networks in a toy domain, to larger and deeper recurrent networks dealing with real-life data, and may therefore contribute to a better understanding of the internal dynamics of current state-of-the-art models in natural language processing.

This communication is accomplished via and Scheutz in preparation). The observed naturallytimed strategies which necessarily incorporate time. The interaction interaction is used to argue that in at least some interesting between the agents is naturally extended over time, yet interactive situations, explicit representation of or in neither agent does any explicit representation of or reasoning operation on time is not necessary. Observing that many interactive about time occur. Kelso et al's Virtual Partner Interaction situations will be similar, we hypothesize that in (Kelso et al. 2009) is a paradigm in which a virtual fact most interactions will require no explicit representation hand is guided by a dynamical system known to guide most or reasoning about time.