LBGP: Learning Based Goal Planning for Autonomous Following in Front

• URL: http://arxiv.org/abs/2011.03125v1
• Date: Thu, 5 Nov 2020 22:29:30 GMT
• Title: LBGP: Learning Based Goal Planning for Autonomous Following in Front
• Authors: Payam Nikdel, Richard Vaughan, Mo Chen
• Abstract summary: This paper investigates a hybrid solution which combines deep reinforcement learning (RL) and classical trajectory planning. An autonomous robot aims to stay ahead of a person as the person freely walks around. 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.
• Score: 16.13120109400351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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