作者: Yuval Tassa , Tom Erez , Emanuel Todorov
DOI: 10.1109/IROS.2012.6386025
关键词: Robot 、 Differential dynamic programming 、 Humanoid robot 、 Software 、 Trajectory optimization 、 Stability (learning theory) 、 Trajectory 、 Model predictive control 、 Control theory 、 Engineering 、 Control engineering
摘要: We present an online trajectory optimization method and software platform applicable to complex humanoid robots performing challenging tasks such as getting up from arbitrary pose on the ground recovering large disturbances using dexterous acrobatic maneuvers. The resulting behaviors, illustrated in attached video, are computed only 7 × slower than real time, a standard PC. video also shows results acrobot problem, planar swimming one-legged hopping. These simpler problems can already be solved without pre-computing anything.