ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation
Fei Xia,Chengshu Li,Roberto Martín-Martín,Or Litany,Alexander Toshev,Silvio Savarese,Fei Xia,Chengshu Li,Roberto Martín-Martín,Or Litany,Alexander Toshev,Silvio Savarese
Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action space to a higher level in the form of subgoals for a motion generator (a combination of motion planner and trajectory executor). We argue that, by lifting the action space and by leveraging sampling-based motion planners...
