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

2020/10/16 by Xin Ye, Ye, Xin, Yezhou Yang +1 · 4 citations
Computer Science · #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Optimization and Search Problems #Reinforcement Learning in Robotics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2010.08596

openalex publication_date 2020/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Modular Multitask Reinforcement Learning with Policy Sketches
• Building Generalizable Agents with a Realistic and Rich 3D Environment
• Data-Efficient Hierarchical Reinforcement Learning
• Hierarchical Reinforcement Learning with Hindsight
• On Evaluation of Embodied Navigation Agents
• Near-Optimal Representation Learning for Hierarchical Reinforcement\n Learning
• Visual Semantic Navigation using Scene Priors
• Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?

Cited by

• N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
• TopoNav: Topological Graphs as a Key Enabler for Advanced Object Navigation
• Distilling LLM Prior to Flow Model for Generalizable Agent's Imagination in Object Goal Navigation
• UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents

Related

• Hierarchical Policy Learning for Mechanical Search
• A survey on policy search algorithms for learning robot controllers in a\n handful of trials
• GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment
• Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots
• HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward