Point Cloud Registration via Heuristic Reward Reinforcement Learning
نویسندگان
چکیده
This paper proposes a heuristic reward reinforcement learning framework for point cloud registration. As an essential step of many 3D computer vision tasks such as object recognition and reconstruction, registration has been well studied in the existing literature. contributes to literature by addressing limitations embedding functions methods. An improved state-embedding module stochastic function are proposed. While enriches captured characteristics states, newly designed follows time-dependent searching strategy, which allows aggressive attempts at beginning tends be conservative end. We assess our method based on two public datasets (ModelNet40 ScanObjectNN) real-world data. The results confirm strength new reducing errors rotation translation, leading more precise
منابع مشابه
Reinforcement learning with via-point representation
In this paper, we propose a new learning framework for motor control. This framework consists of two components: reinforcement learning and via-point representation. In the field of motor control, conventional reinforcement learning has been used to acquire control sequences such as cart-pole or stand-up robot control. Recently, researchers have become interested in hierarchical architecture, s...
متن کاملImproving Probabilistic Image Registration via Reinforcement Learning and Uncertainty Evaluation
One framework for probabilistic image registration involves assigning probability distributions over spatial transformations (e.g. distributions over displacement vectors at each voxel). In this paper, we propose an uncertainty measure for these distributions that examines the actual spatial displacements, thus departing from the classical Shannon entropy-based measures, which examine only the ...
متن کاملReward, Motivation, and Reinforcement Learning
There is substantial evidence that dopamine is involved in reward learning and appetitive conditioning. However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly ...
متن کاملCompatible Reward Inverse Reinforcement Learning
PROBLEM • Inverse Reinforcement Learning (IRL) problem: recover a reward function explaining a set of expert’s demonstrations. • Advantages of IRL over Behavioral Cloning (BC): – Transferability of the reward. • Issues with some IRL methods: – How to build the features for the reward function? – How to select a reward function among all the optimal ones? – What if no access to the environment? ...
متن کاملAn Average - Reward Reinforcement Learning
Recently, there has been growing interest in average-reward reinforcement learning (ARL), an undiscounted optimality framework that is applicable to many diierent control tasks. ARL seeks to compute gain-optimal control policies that maximize the expected payoo per step. However, gain-optimality has some intrinsic limitations as an optimality criterion, since for example, it cannot distinguish ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Stats
سال: 2023
ISSN: ['2571-905X']
DOI: https://doi.org/10.3390/stats6010016