Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction

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A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms t ...

For robots to operate in unstructured environments, they are required to interact with objects through contact. Those contacts may be used to push objects to the side, deform objects, or manipulate objects in-hand. This thesis addresses the problem of cont ...

Industrial robots have been programmed to optimize mass production by executing predefined trajectories with their high speed, precision, and repeatability over long production cycles. However, the rise of Industry 4.0 and the increasing demand for diverse ...

A shortcut model is a low-order model that provides a simplified representation of a complex process, thereby enabling faster and more efficient analysis, design and optimization without the computational intensity of a full-scale model. For continuous dis ...

Fuel performance codes are essential for predicting fuel rod behavior in nuclear reactors. These codes comprise a complex system of models with empirical constants that require calibration against experimental data. To support this calibration, this study ...

Photorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be easily applied to robotics because they typically lack physics-related informa ...

Implementing virtual fixtures in guiding tasks constrains the movement of the robot's end effector to specific curves within its workspace. However, incorporating guiding frameworks may encounter discontinuities when optimizing the reference target positio ...

In this paper, we present EdgeFace - a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achi ...

Byzantine agreement enables n processes to agree on a common L-bit value, despite up to t > 0 arbitrary failures. A long line of work has been dedicated to improving the bit complexity of Byzantine agreement in synchrony. This has culminated in COOL, an er ...

We formulate computed-tomography reconstruction as a continuous-domain optimization problem with Hessian total variation (HTV) as the regularizer. HTV is a sparsity-promoting regularizer that favors continuous and piecewise-linear functions with few affine ...

Large Language Models (LLMs) present a promising frontier in robotic task planning by leveraging extensive human knowledge. Nevertheless, the current literature often overlooks the critical aspects of robots' adaptability and error correction. This work ai ...

In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM outputs (a.k ...

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy ...

We survey lower-bound results in complexity theory that have been obtained via newfound interconnections between propositional proof complexity, boolean circuit complexity, and query/communication complexity. We advocate for the theory of total search prob ...

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

Microbubbles excited by acoustic fields inside water oscillate, and generate acoustic radiation forces and drag-induced acoustic streaming. These forces can be harnessed in various biomedical applications such as targeted drug delivery and on-chip biomanip ...

Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction

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Principles and Guidelines for Evaluating Social RobotNavigation Algorithms

A Stochastic Approach to Contact-rich Manipulation

An augmented reality-based wearable system for handheld-free and intuitive robot programming

A shortcut model for batch distillation columns using pertinent effective factors

Preliminary Sensitivity Analysis of Time-Dependent Code Outputs in Fuel Performance Models Using OFFBEAT for Future Calibration

GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim

A Minimum-Jerk Approach to Handle Singularities in Virtual Fixtures

EdgeFace: Efficient Face Recognition Model for Edge Devices

Efficient Signature-Free Validated Agreement

A Continuous-Domain Solution for Computed Tomography with Hessian Total-Variation Regularization

Action Contextualization: Adaptive Task Planning and Action Tuning Using Large Language Models

Trustworthy and Efficient LLMs Meet Databases

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

Experimental data-driven model predictive control of a hospital HVAC system during regular use

Proofs, Circuits, and Communication

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Dynamics of entrapped microbubbles with multiple openings