LBGP: Learning Based Goal Planning for Autonomous Following in Front
This addresses the challenge of reliable human-robot interaction in dynamic environments, though it is incremental as it builds on existing RL and planning methods.
The paper tackles the problem of an autonomous robot following in front of a person by combining deep reinforcement learning for trajectory estimation and classical planning for navigation, achieving state-of-the-art performance with zero-shot transfer from simulation to real-world.
This paper investigates a hybrid solution which combines deep reinforcement learning (RL) and classical trajectory planning for the following in front application. Here, an autonomous robot aims to stay ahead of a person as the person freely walks around. Following in front is a challenging problem as the user's intended trajectory is unknown and needs to be estimated, explicitly or implicitly, by the robot. In addition, the robot needs to find a feasible way to safely navigate ahead of human trajectory. Our deep RL module implicitly estimates human trajectory and produces short-term navigational goals to guide the robot. These goals are used by a trajectory planner to smoothly navigate the robot to the short-term goals, and eventually in front of the user. We employ curriculum learning in the deep RL module to efficiently achieve a high return. Our system outperforms the state-of-the-art in following ahead and is more reliable compared to end-to-end alternatives in both the simulation and real world experiments. In contrast to a pure deep RL approach, we demonstrate zero-shot transfer of the trained policy from simulation to the real world.