A5069176311- Reinforcement Learning in Robotics
- Wound Healing and Treatments
- Robotic Locomotion and Control
- Robot Manipulation and Learning
- Surgical site infection prevention
- Surgical Sutures and Adhesives
Beijing Institute of Technology
2024
Reinforcement learning recently has achieved impressive success in allowing robots to learn complex motor skills simulation environments. However, most of these successes are difficult transfer physical since current algorithms require lots practical training and sim-to-real skills. To improve the efficiency adaptability robots, this article proposes a guided model-based policy search (GMBPS) algorithm inspired by hypothetical model-free (MF) (MB) actor-critic brain implementation. This...
Purpose Current reinforcement learning (RL) algorithms are facing issues such as low efficiency and poor generalization performance, which significantly limit their practical application in real robots. This paper aims to adopt a hybrid model-based model-free policy search method with multi-timescale value function tuning, aiming allow robots learn complex motion planning skills multi-goal multi-constraint environments few interactions. Design/methodology/approach A goal-conditioned tuning...
Objective: To investigate the effect of different negative pressure wound dressing (NPD) on survival full-thickness skin grafts patients. Methods: One hundred and eleven patients who need grafting, conforming to inclusion criteria were hospitalized in our unit from August 2012 March 2017, their clinical data retrospectively analyzed. Forty-seven October 2015 assigned into traditional treatment group. Sixty-four November 2017 divided -9.975 kPa group (n=34) -13.300 (n=30). Patients received...