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We propose that the machinability of hard ceramics can be improved by reversible electrochemical doping. On the example of TiO2, we show in a combined density functional theory-molecular dynamics computational study that a small amount of…
We theoretically investigate the biomechanical aspects of Olympic weightlifting within the framework of optimal control theory. The squared force and the rate of force development (RFD) defined by the time derivative of the force are taken…
This paper examines strategic effort and positioning choices resulting in bandwagon effects under externalities in finite multi-stage games using causal evidence from triathlon (Reichel, 2025). Focusing on open-water swim drafting where…
At the future electron-positron TeV linear collider, the reachable physics will be strongly dependent on the detector capability to reconstruct high energy jets in multi-jet environment. At LEP, SLD experiments, a technique combining…
Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging,…
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety-critical applications. A number of defense methods have been proposed to train robust DNNs resistant…
Ultra intense lasers are a promising source of energetic ions for various applications. An interesting approach described in Ferri et al. 2019 argues from Particle-in-Cell simulations that using two laser pulses of half energy (half…
Conventional robot programming methods are complex and time-consuming for users. In recent years, alternative approaches such as mixed reality have been explored to address these challenges and optimize robot programming. While the findings…
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail. To effectively reflect this challenging category of…
In this work, we delve into the intricate synergy among non-prehensile actions like pushing, and prehensile actions such as grasping and throwing, within the domain of robotic manipulation. We introduce an innovative approach to learning…
In this work, we present an approach to minimizing the time necessary for the end-effector of a redundant robot manipulator to traverse a Cartesian path by optimizing the trajectory of its joints. Each joint has limits in the ranges of…
Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throwing-catching behavior has the…
Joint attention - the ability to purposefully coordinate attention with another agent, and mutually attend to the same thing -- is a critical component of human social cognition. In this paper, we ask whether joint attention can be useful…
We investigate the transport through a nanoscale device consisting of a degenerate double-orbital Anderson dot coupled to two uncorrelated leads. We determine the thermoelectric transport properties close to the one-electron regime and…
Although it has been shown that augmented multimodal feedback has a facilitatory effect on motor performance for motor learning and music training, the functionality of haptic feedback combined with other modalities in rhythmic movement…
Training on edge devices poses several challenges as these devices are generally resource-constrained, especially in terms of power. State-of-the-art techniques at the device level reduce the GPU frequency to enforce power constraints,…
This paper proposes a two-time scale neurodynamic duplex approach to solve distributionally robust geometric joint chance-constrained optimization problems. The probability distributions of the row vectors are not known in advance and…
In this research, we investigated the innovative use of a manipulator as a tail in quadruped robots to augment their physical capabilities. Previous studies have primarily focused on enhancing various abilities by attaching robotic tails…
Reduced traction limits the ability of mobile robotic systems to resist or apply large external loads, such as tugging a massive payload. One simple and versatile solution is to wrap a tether around naturally occurring objects to leverage…
Voxel-grid reinforcement learning is widely adopted for path planning in redundant manipulators due to its simplicity and reproducibility. However, direct execution through point-wise numerical inverse kinematics on 7-DoF arms often yields…