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...