While MPC enables nonlinear feedback control by solving an optimal control problem at each timestep, the computational burden tends to be significantly large, making it difficult to optimize a policy within the control period. To address this issue, one possible approach is to utilize terminal value learning to reduce computational costs. However, the learned value cannot be used for other tasks in situations where the task dynamically changes in the original MPC setup. In this study, we develop an MPC framework with goal-conditioned terminal value learning to achieve multitask policy optimization while reducing computational time. Furthermore, by using a hierarchical control structure that allows the upper-level trajectory planner to output appropriate goal-conditioned trajectories, we demonstrate that a robot model is able to generate diverse motions. We evaluate the proposed method on a bipedal inverted pendulum robot model and confirm that combining goal-conditioned terminal value learning with an upper-level trajectory planner enables real-time control; thus, the robot successfully tracks a target trajectory on sloped terrain.

Extreme cornering in racing often induces large side-slip angles, presenting a formidable challenge in vehicle control. To tackle this issue, this paper introduces an Active Exploration with Double GPR (AEDGPR) system. The system initiates by planning a minimum-time trajectory with a Gaussian Process Regression(GPR) compensated model. The planning results show that in the cornering section, the yaw angular velocity and side-slip angle are in opposite directions, indicating that the vehicle is drifting. In response, we develop a drift controller based on Model Predictive Control (MPC) and incorporate Gaussian Process Regression to correct discrepancies in the vehicle dynamics model. Moreover, the covariance from the GPR is employed to actively explore various cornering states, aiming to minimize trajectory tracking errors. The proposed algorithm is validated through simulations on the Simulink-Carsim platform and experiments using a 1/10 scale RC vehicle.

Error correction codes (ECC) are crucial for ensuring reliable information transmission in communication systems. Choukroun & Wolf (2022b) recently introduced the Error Correction Code Transformer (ECCT), which has demonstrated promising performance across various transmission channels and families of codes. However, its high computational and memory demands limit its practical applications compared to traditional decoding algorithms. Achieving effective quantization of the ECCT presents significant challenges due to its inherently small architecture, since existing, very low-precision quantization techniques often lead to performance degradation in compact neural networks. In this paper, we introduce a novel acceleration method for transformer-based decoders. We first propose a ternary weight quantization method specifically designed for the ECCT, inducing a decoder with multiplication-free linear layers. We present an optimized self-attention mechanism to reduce computational complexity via codeaware multi-heads processing. Finally, we provide positional encoding via the Tanner graph eigendecomposition, enabling a richer representation of the graph connectivity. The approach not only matches or surpasses ECCT's performance but also significantly reduces energy consumption, memory footprint, and computational complexity. Our method brings transformer-based error correction closer to practical implementation in resource-constrained environments, achieving a 90% compression ratio and reducing arithmetic operation energy consumption by at least 224 times on modern hardware.

The complex ways in which humans utilize their bodies in sports and martial arts are remarkable, and human motion analysis is one of the most effective tools for robot body design and control. On the other hand, motion analysis is not easy, and it is difficult to measure complex body motions in detail due to the influence of numerous muscles and soft tissues, mainly ligaments. In response, various musculoskeletal simulators have been developed and applied to motion analysis and robotics. However, none of them reproduce the ligaments but only the muscles, nor do they focus on the shoulder complex, including the clavicle and scapula, which is one of the most complex parts of the body. Therefore, in this study, a detailed simulation model of the shoulder complex including ligaments is constructed. The model will mimic not only the skeletal structure and muscle arrangement but also the ligament arrangement and maximum muscle strength. Through model predictive control based on the constructed simulation, we confirmed that the ligaments contribute to joint stabilization in the first movement and that the proper distribution of maximum muscle force contributes to the equalization of the load on each muscle, demonstrating the effectiveness of this simulation.

Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing attention in this field, but experimental data for training are often scarce, which hampers their application. In this work, we introduce a novel generic approach for improving data-driven models: inspired by the ancient rule "similia similibus solvuntur", we lump components that behave similarly into chemical classes and model them jointly in the first step of a hierarchical approach. While the information on class affiliations can stem in principle from any source, we demonstrate how classes can reproducibly be defined based on mixture data alone by agglomerative clustering. The information from this clustering step is then used as an informed prior for fitting the individual data. We demonstrate the benefits of this approach by applying it in connection with a matrix completion method (MCM) for predicting isothermal activity coefficients at infinite dilution in binary mixtures. Using clustering leads to significantly improved predictions compared to an MCM without clustering. Furthermore, the chemical classes learned from the clustering give exciting insights into what matters on the molecular level for modeling given mixture properties.

Deep learning models have demonstrated remarkable success in various fields, including seismology. However, one major challenge in deep learning is the presence of mislabeled examples. Additionally, accurately estimating model uncertainty is another challenge in machine learning. In this study, we develop Convolutional Neural Networks (CNNs) to classify seismic waveforms based on first-motion polarity. We trained multiple CNN models with different settings. We also constructed ensembles of networks to estimate uncertainty. The results showed that each training setting achieved satisfactory performances, with the ensemble method outperforming individual networks in uncertainty estimation. We observe that the uncertainty estimation ability of the ensembles of networks can be enhanced using dropout layers. In addition, comparisons among different training settings revealed that the use of dropout improved the robustness of networks to mislabeled examples.

Multiobject tracking (MOT) is an important task in robotics, autonomous driving, and maritime surveillance. Traditional work on MOT is model-based and aims to establish algorithms in the framework of sequential Bayesian estimation. More recent methods are fully data-driven and rely on the training of neural networks. The two approaches have demonstrated advantages in certain scenarios. In particular, in problems where plenty of labeled data for the training of neural networks is available, data-driven MOT tends to have advantages compared to traditional methods. A natural thought is whether a general and efficient framework can integrate the two approaches. This paper advances a recently introduced hybrid model-based and data-driven method called neural-enhanced belief propagation (NEBP). Compared to existing work on NEBP for MOT, it introduces a novel neural architecture that can improve data association and new object initialization, two critical aspects of MOT. The proposed tracking method is leading the nuScenes LiDAR-only tracking challenge at the time of submission of this paper.

Causal structure discovery methods are commonly applied to structured data where the causal variables are known and where statistical testing can be used to assess the causal relationships. By contrast, recovering a causal structure from unstructured natural language data such as news articles contains numerous challenges due to the absence of known variables or counterfactual data to estimate the causal links. Large Language Models (LLMs) have shown promising results in this direction but also exhibit limitations. This work investigates LLM's abilities to build causal graphs from text documents and perform counterfactual causal inference. We propose an end-to-end causal structure discovery and causal inference method from natural language: we first use an LLM to extract the instantiated causal variables from text data and build a causal graph. We merge causal graphs from multiple data sources to represent the most exhaustive set of causes possible. We then conduct counterfactual inference on the estimated graph. The causal graph conditioning allows reduction of LLM biases and better represents the causal estimands. We use our method to show that the limitations of LLMs in counterfactual causal reasoning come from prediction errors and propose directions to mitigate them. We demonstrate the applicability of our method on real-world news articles.

Contact-rich manipulation remains a major challenge in robotics. Optical tactile sensors like GelSight Mini offer a low-cost solution for contact sensing by capturing soft-body deformations of the silicone gel. However, accurately inferring shear and normal force distributions from these gel deformations has yet to be fully addressed. In this work, we propose a machine learning approach using a U-net architecture to predict force distributions directly from the sensor's raw images. Our model, trained on force distributions inferred from Finite Element Analysis (FEA), demonstrates promising accuracy in predicting normal and shear force distributions. It also shows potential for generalization across sensors of the same type and for enabling real-time application. The codebase, dataset and models are open-sourced and available at https://feats-ai.github.io .

We consider the discrete time infinite horizon average reward restless markovian bandit (RMAB) problem. We propose a \emph{model predictive control} based non-stationary policy with a rolling computational horizon $\tau$. At each time-slot, this policy solves a $\tau$ horizon linear program whose first control value is kept as a control for the RMAB. Our solution requires minimal assumptions and quantifies the loss in optimality in terms of $\tau$ and the number of arms, $N$. We show that its sub-optimality gap is $O(1/\sqrt{N})$ in general, and $\exp(-\Omega(N))$ under a local-stability condition. Our proof is based on a framework from dynamic control known as \emph{dissipativity}. Our solution easy to implement and performs very well in practice when compared to the state of the art. Further, both our solution and our proof methodology can easily be generalized to more general constrained MDP settings and should thus, be of great interest to the burgeoning RMAB community.