Results 171 to 180 of about 6,817,972 (312)
Lessons learned from COVID-19 and the implications for resilience research, policy and practice. [PDF]
Fischbacher-Smith D, Adekola J.
europepmc +1 more source
Auditory–Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients
In this work, we explore auditory–tactile congruence for synthesizing adaptive vocal pain expressions in robopatients. Using a robopatient platform that integrates vocal pain sounds with palpation forces, we conducted 7680 trials across 20 participants.
Saitarun Nadipineni +4 more
wiley +1 more source
Performance Metrics for Measles Response Optimization in the United States: Considerations for Public Health Research, Policy, and Practice. [PDF]
Ravi SJ +4 more
europepmc +1 more source
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 +2 more
wiley +1 more source
Arts and Health Glossary - A Summary of Definitions for Use in Research, Policy and Practice. [PDF]
Davies CR, Clift S.
europepmc +1 more source
Africa Research in Sustainable Intensification for the Next Generation: Monitoring and evaluation scope of work for Phase II (2016-2021) [PDF]
International Food Policy Research Institute
core
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
Recognition of Children's Learning in Educational Research, Policy and Practice: Herbison Invited Lecture, NZARE Annual Conference 2022. [PDF]
O'Neill J.
europepmc +1 more source
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao +4 more
wiley +1 more source

