POMDP-based Object Search with Growing State Space and Hybrid Action Domain
The paper introduces a POMDP-based object search method GNPF-kCT, significantly improving target localization speed.
Key Findings
Methodology
The paper proposes a novel online POMDP solver called GNPF-kCT. This method combines Monte Carlo Tree Search (MCTS) with a neural process network to filter useless primitive actions and uses k-center clustering hypersphere discretization for efficient refinement of high-dimensional action spaces. Additionally, a modified upper-confidence bound (UCB) guides MCTS expansion.
Key Results
- In Gazebo simulations, Fetch and Stretch robots achieve 20% faster target localization than POMDP-based baselines under the same computational constraints.
- Demonstrates higher reliability compared to state-of-the-art non-POMDP solvers, especially large language model-based methods.
- Real-world office environment tests confirm the method's practical applicability.
Significance
This research holds significant implications for academia and industry, addressing long-standing challenges in quickly locating target objects in complex indoor environments, particularly with visual occlusion and localization errors. The method not only enhances search efficiency but also opens new possibilities for robotic applications in dynamic and uncertain environments.
Technical Contribution
The technical contributions include a new online POMDP solver capable of handling growing state spaces and hybrid action domains. By introducing neural process filtering and k-center clustering, the method significantly improves solving efficiency and performance. Theoretical analysis validates the convergence and performance potential.
Novelty
This method is the first to employ neural process networks for action filtering in POMDP solving, significantly enhancing the handling of high-dimensional action spaces compared to existing methods. Unlike traditional approaches, this method dynamically adapts to growing state spaces.
Limitations
- Performance may degrade in scenarios with limited information or rewards, as the guessed target object strategy may be inaccurate in some cases.
- Computational costs may increase in very complex environments.
Future Work
Future research directions include optimizing computational efficiency, extending the method to handle more complex environments, and testing on more types of robotic platforms.
AI Executive Summary
In complex indoor environments, mobile robots face challenges in locating target objects, especially with visual occlusion and localization errors. Existing methods struggle with high-dimensional state spaces and hybrid action domains.
This paper introduces a POMDP-based object search method called GNPF-kCT. The method combines Monte Carlo Tree Search and neural process networks to filter useless primitive actions and uses k-center clustering hypersphere discretization for efficient refinement of high-dimensional action spaces. A modified upper-confidence bound guides MCTS expansion, with theoretical analysis validating the method's convergence and performance potential.
In Gazebo simulations, Fetch and Stretch robots achieve 20% faster target localization than POMDP-based baselines under the same computational constraints. The method demonstrates higher reliability compared to state-of-the-art non-POMDP solvers, especially large language model-based methods. Real-world office environment tests confirm the method's practical applicability.
Deep Dive
Abstract
Efficiently locating target objects in complex indoor environments with diverse furniture, such as shelves, tables, and beds, is a significant challenge for mobile robots. This difficulty arises from factors like localization errors, limited fields of view, and visual occlusion. We address this by framing the object-search task as a highdimensional Partially Observable Markov Decision Process (POMDP) with a growing state space and hybrid (continuous and discrete) action spaces in 3D environments. Based on a meticulously designed perception module, a novel online POMDP solver named the growing neural process filtered k-center clustering tree (GNPF-kCT) is proposed to tackle this problem. Optimal actions are selected using Monte Carlo Tree Search (MCTS) with belief tree reuse for growing state space, a neural process network to filter useless primitive actions, and k-center clustering hypersphere discretization for efficient refinement of high-dimensional action spaces. A modified upper-confidence bound (UCB), informed by belief differences and action value functions within cells of estimated diameters, guides MCTS expansion. Theoretical analysis validates the convergence and performance potential of our method. To address scenarios with limited information or rewards, we also introduce a guessed target object with a grid-world model as a key strategy to enhance search efficiency. Extensive Gazebo simulations with Fetch and Stretch robots demonstrate faster and more reliable target localization than POMDP-based baselines and state-of-the-art (SOTA) non-POMDP-based solvers, especially large language model (LLM) based methods, in object search under the same computational constraints and perception systems. Real-world tests in office environments confirm the practical applicability of our approach. Project page: https://sites.google.com/view/gnpfkct.