Results 181 to 190 of about 34,146 (315)
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
Investigation of closed form solitons for the stochastic Chavy-Waddy-Kolokolnikov equation in bacterial aggregation. [PDF]
Nawaz S +4 more
europepmc +1 more source
Echinoderm‐Inspired Autonomy for Soft‐Legged Robots
Inspired by echinoderms, a modular soft robot achieves autonomous phototaxis without a central controller or explicit communication. Each limb independently adapts its actuation timing through local sensing and short‐term memory. Coordination emerges purely from physical interactions, demonstrating resilience to changes in morphology, environment, and ...
Harmannus A. H. Schomaker +2 more
wiley +1 more source
Modeling and analysis of stochastic quantum magnetohydrodynamics equations with energy estimates. [PDF]
Divyabala K, Durga N.
europepmc +1 more source
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 +8 more
wiley +1 more source
How to Solve Dynamic Stochastic Models Computing Expectations Just Once [PDF]
We introduce a technique called "precomputation of integrals" that makes it possible to compute conditional expectations in dynamic stochastic models in the initial stage of the solution procedure.
Lilia Maliar +2 more
core
A Random Field Theory of Electromagnetic Information. [PDF]
Mikki S.
europepmc +1 more source
LLM‐Integrated Human–Robot Interaction System for Microrobots
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley +1 more source
Determining disease attributes from epidemic trajectories. [PDF]
Rast MP, Rast LI.
europepmc +1 more source
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano +3 more
wiley +1 more source

