Abstract
Investigators discuss new findings in Robotics. According to news originating from Xi'an, People's Republic of China, by NewsRx correspondents, research stated, "A novel efficient reinforcement lear ning paradigm combining human knowledge, model-based and model-free methods is p resented for optimal robot manipulation control during complex multi-phase robot manipulation tasks, e.g., the peg-inhole tasks with tight fit and nut-and-bolt assembly. Firstly, human demonstration is conducted to collect the data during successful robot manipulation, and manipulation phase estimation method integrat ing with human knowledge is presented to obtain the higher-level planning of the multi-phase robot manipulation tasks." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Guangdong Major Project of Basic and Applied Basic Research.
NETL
NSTL