论文标题
通过HIEM进行高效的机器人对象搜索：层次策略学习，并具有固有的超支建模
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling
论文作者
论文摘要
尽管在实现自主行为的机器人方面取得了重大成功，这使得深刻的强化学习成为机器人对象搜索任务的有前途的方法，但深厚的强化学习方法严重遭受了任务的稀疏奖励设置。为了应对这一挑战，我们基于层次结构且可解释的建模，为对象搜索任务提供了一种新颖的政策学习范式，并具有内在的超级奖励设置。更具体地说，我们通过代理低级政策有效地探索环境，该政策由内在的奖励子目标驱动。我们从高效的探索体验中进一步学习了分层政策，在这些体验中，我们优化了高级和低级政策，以实现外在的奖励目标，以很好地执行对象搜索任务。在House3D环境上进行的实验验证，并表明经过我们模型训练的机器人可以以更优化和可解释的方式执行对象搜索任务。
Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinforcement learning approach severely suffers from the nature sparse reward setting of the task. To tackle this challenge, we present a novel policy learning paradigm for the object search task, based on hierarchical and interpretable modeling with an intrinsic-extrinsic reward setting. More specifically, we explore the environment efficiently through a proxy low-level policy which is driven by the intrinsic rewarding sub-goals. We further learn our hierarchical policy from the efficient exploration experience where we optimize both of our high-level and low-level policies towards the extrinsic rewarding goal to perform the object search task well. Experiments conducted on the House3D environment validate and show that the robot, trained with our model, can perform the object search task in a more optimal and interpretable way.
