Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling

Abstract

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.

arXiv commentRA-L & ICRA21

References in corpus (17)

• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
• On Evaluation of Embodied Navigation Agents
• Building Generalizable Agents with a Realistic and Rich 3D Environment
• Data-Efficient Hierarchical Reinforcement Learning
• Modular Multitask Reinforcement Learning with Policy Sketches
• Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
• Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
• Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
• Benchmarking Classic and Learned Navigation in Complex 3D Environments
• Hierarchical Reinforcement Learning with Hindsight
• Visual Semantic Navigation using Scene Priors
• Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
• Hierarchical Imitation and Reinforcement Learning
• Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
• From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN)
• To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments
• Hierarchical Policy Learning is Sensitive to Goal Space Design