Results 261 to 270 of about 108,016 (310)

A State‐Adaptive Koopman Control Framework for Real‐Time Deformable Tool Manipulation in Robotic Environmental Swabbing

Advanced Robotics Research, EarlyView.
This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi, Amirreza Davar, Dongyi Wang   +2 more
wiley   +1 more source

ChicGrasp: Imitation‐Learning‐Based Customized Dual‐Jaw Gripper Control for Manipulation of Delicate, Irregular Bio‐Products

Advanced Robotics Research, EarlyView.
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Glen Crandall, Dongyi Wang   +8 more
wiley   +1 more source

Liquid Crystalline Elastomers in Soft Robotics: Assessing Promise and Limitations

Advanced Robotics Research, EarlyView.
Liquid crystalline elastomers (LCEs) are programmable soft materials that undergo large, anisotropic deformation in response to external stimuli. Their molecular alignment encodes directional actuation in a monolithic structure, making them long‐standing candidates for soft robotic systems.
Justin M. Speregen, Timothy J. White
wiley   +1 more source

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Backpropagation Through Soft Body: Investigating Information Processing in Brain–Body Coupling Systems

Advanced Robotics Research, EarlyView.
This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka, Katsuma Inoue, Yasuo Kuniyoshi, Kohei Nakajima   +3 more
wiley   +1 more source

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions. [PDF]

Ultrason Sonochem
Li S, Tsui PH, Wu W, Zhou Z, Wu S.
europepmc   +1 more source

A Soft Robotic Fish With a Dielectric Elastomer Actuator Body and Negative Stiffness Spine

Advanced Robotics Research, EarlyView.
This work introduces a bio‐mimetic soft robotic fish driven by fiber‐reinforced dielectric elastomer actuators integrated as its body. By prestretching this active skin against a flexible spine, a negative stiffness system is created, enabling large‐amplitude bending.
Markus Koenigsdorff, Simon Holzer, Stefania Konstantinidi, Yoan Civet, Yves Perriard   +4 more
wiley   +1 more source

An information criterion for variable selection in Support Vector Machines. [PDF]

Croux, Christophe, Claeskens, Gerda, Van Kerckhoven, Johan   +2 more
core  

Support Vector Machines [PDF]

The Stata Journal, 2016
Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines.
Matthias Schönlau
exaly   +2 more sources

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Support vector machines in A+

ACM SIGAPL APL Quote Quad, 2004
This paper considers the implementation of Support Vector Machines (SVM), the new extensive class of data analysis methods. SVM have a number of advantages as compared with standard data mining techniques like artificial neural networks, for example. In the paper this methodology is described in details and implemented in A+ programming language in the
Alexander O. Skomorokhov, Alexander Nakhabov   +1 more
openaire   +1 more source