Search · four archives
Search · four archives
22 papers · ranked by Valyu relevance
Elmar Rückert, Andrea d'Avella
A salient feature of human motor skill learning is the ability to exploit similarities across related tasks. In biological motor control, it has been hypothesized that muscle synergies, coherent activations of groups of muscles, allow for exploiting shared knowledge. Recent studies have shown that a rich set of complex…
Neville Hogan, Dagmar Sternad
Humans achieve locomotor dexterity that far exceeds the capability of modern robots, yet this is achieved despite slower actuators, imprecise sensors, and vastly slower communication. We propose that this spectacular performance arises from encoding motor commands in terms of dynamic primitives. We propose three…
Till Hielscher, Andreas Bulling, Kai O. Arras
— Our goal is to enable social robots to interact autonomously with humans in a realistic, engaging, and expressive manner. The 12 Principles of Animation [1] are a well-established framework animators use to create movements that make characters appear convincing, dynamic, and emotionally expressive. This paper…
Moses C. Nah, Johannes Lachner, Neville Hogan, Jean-Jacques Slotine
— This paper presents a modular framework for motion planning using movement primitives. Central to the approach is Contraction Theory, a modular stability tool for nonlinear dynamical systems. The approach extends prior methods by achieving parallel and sequential combinations of both discrete and rhythmic movements…
Jože M Rožanec, Bojan Nemec
One of the most important challenges in robotics is producing accurate trajectories and controlling their dynamic parameters so that the robots can perform different tasks. The ability to provide such motion control is closely related to how such movements are encoded. Advances on deep learning have had a strong…
Benjamin Parrell, Adam C. Lammert
Current models of speech motor control rely on either trajectory-based control (DIVA, GEPPETO, ACT) or a dynamical systems approach based on feedback control (Task Dynamics, FACTS). While both approaches have provided insights into the speech motor system, it is difficult to connect these findings across models given…
Franziska Meier, Stefan Schaal
Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforcement learning, movement recognition and segmentation and control. Because of this they have become a popular representation for motor primitives. In this work, we showcase how DMPs can be…
T. Warren Liao, Ge Li, Hongyi Zhou, Rudolf Lioutikov + 1 more
'Gerhard Neumann'] Abstract: This work introduces B-spline Movement Primitives (BMPs), a new Movement Primitive (MP) variant that leverages B-splines for motion representation. B-splines are a well-known concept in motion planning due to their ability to generate complex, smooth trajectories with only a few control…
Lennart Jahn, Florentin Wörgötter, Tomas Kulviĉius
— Movement generation, and especially generalisation to unseen situations, plays an important role in robotics. Different types of movement generation methods exist such as spline based methods, dynamical system based methods, and methods based on Gaussian mixture models (GMMs). Using a large, new dataset on human…
Carlo Tiseo, Sydney Rebecca Charitos, Michael Mistry
Humans can robustly interact with external dynamics that are not yet fully understood. This work presents a hierarchical architecture of semi-autonomous controllers that can control the redundant kinematics of the limbs during dynamic interaction, even with delays comparable to the nervous system. The postural…
Andrea d'Avella, Martin Giese, Yuri P. Ivanenko, Thomas Schack + 1 more
A number of contributions aim at understanding motor primitives at the kinematic level. Zelman et al. () explore whether different octopus arm movements are built up of elementary kinematic units by decomposing surfaces, representing curvature, and torsion values of the paths of points along the arm, into a weighted…
Francesco Donnarumma, Roberto Prevete, Domenico Maisto, Andrea Fuscone + 3 more
Animal behavior is highly structured. Yet, structured behavioral patterns – or “statistical ethograms” – are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to characterize quantitatively rodent behavior during spatial (e.g., maze)…
Tim Waegeman, Michiel Hermans, Benjamin Schrauwen
Walking, catching a ball and reaching are all tasks in which humans and animals exhibit advanced motor skills. Findings in biological research concerning motor control suggest a modular control hierarchy which combines movement/motor primitives into complex and natural movements. Engineers inspire their research on…
Cai Li, Robert Lowe, Tom Ziemke
In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental…
Mirko Raković, Srdjan Savić, José Santos-Victor, Milutin Nikolić + 1 more
'Branislav Borovac'] The focus of research in biped locomotion has moved toward real-life scenario applications, like walking on uneven terrain, passing through doors, climbing stairs and ladders. As a result, we are witnessing significant advances in the locomotion of biped robots, enabling them to move in hazardous…
Lijia Liu, Joseph L. Cooper, Dana H. Ballard
Measurements of human activity are useful for studying the neural computations underlying human behavior. Dynamic models of human behavior also support clinical efforts to analyze, rehabilitate, and predict movements. They are used in biomechanics to understand and diagnose motor pathologies, find new motor strategies…
John Lazzari, Shreya Saxena
The brain and body comprise a complex control system that can flexibly perform a diverse range of movements. Despite the high-dimensionality of the musculoskeletal system, both humans and other species are able to quickly adapt their existing repertoire of actions to novel settings. A strategy likely employed by the…
Luigi Leanza, Claudio Perego, Luca Pesce, Matteo Salvalaglio + 2 more
Mechanically-interlocked molecules (MIMs) are at the basis of artificial molecular machines and are attracting increasing interest for various applications, from catalysis to drug delivery and nanoelectronics. MIMs are composed of mechanically-interconnected molecular sub-parts that can move with respect to each other…
Authors not listed
Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
Ian S. Howard, Laura Alvarez-Hidalgo
The human motor system exhibits remarkable plasticity: not only can we master complex skills, but we can also learn to control artificial effectors. Here, we examine whether haptic force feedback from a simulated endpoint mass facilitates the de novo learning of novel kinematic and dynamic mappings. We investigate this…
Authors not listed
The functionality of viral proteases, such as those from Dengue Virus (DENV) and SARS-CoV-2, is intrinsically linked to their conformational dynamics. While Molecular Dynamics (MD) is a powerful tool to study these motions, its computational cost limits large-scale screening. Here, we present a rapid analysis of the…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…