Partially Observable Markov Decision Processes in Robotics: A Survey

• URL: http://arxiv.org/abs/2209.10342v1
• Date: Wed, 21 Sep 2022 13:24:20 GMT
• Title: Partially Observable Markov Decision Processes in Robotics: A Survey
• Authors: Mikko Lauri, David Hsu, Joni Pajarinen
• Abstract summary: The survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems.
• Score: 23.286897050793435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multi-robot systems, manipulation, and human-robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research.

Related papers

• A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics [16.064583670720587]
  We compare prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models.
  arXiv  Detail & Related papers  (2024-10-30T13:22:55Z)
• R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models [50.19174067263255]
  We introduce prior preference learning techniques and self-revision schedules to help the agent excel in sparse-reward, continuous action, goal-based robotic control POMDP environments. We show that our agents offer improved performance over state-of-the-art models in terms of cumulative rewards, relative stability, and success rate.
  arXiv  Detail & Related papers  (2024-09-21T18:32:44Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• Automated Process Planning Based on a Semantic Capability Model and SMT [50.76251195257306]
  In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. We present an approach that combines these two topics: starting from a semantic capability model, an AI planning problem is automatically generated.
  arXiv  Detail & Related papers  (2023-12-14T10:37:34Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Formal Modelling for Multi-Robot Systems Under Uncertainty [11.21074891465253]
  We review modelling formalisms for multi-robot systems under uncertainty. We discuss how they can be used for planning, reinforcement learning, model checking, and simulation.
  arXiv  Detail & Related papers  (2023-05-26T15:23:35Z)
• Task Allocation using a Team of Robots [29.024300177453824]
  We present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate. We describe how the problem can vary depending on the feasibility constraints, objective functions, and the level of dynamically changing information.
  arXiv  Detail & Related papers  (2022-07-20T04:49:11Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)
• Knowledge-Based Hierarchical POMDPs for Task Planning [0.34998703934432684]
  The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state. In robotics, this is particularly difficult because actions usually have several possible results, and sensors are prone to produce measurements with error. We present a scheme to encode knowledge about the robot and its environment, that promotes the modularity and reuse of information.
  arXiv  Detail & Related papers  (2021-03-19T05:45:05Z)
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms [60.59764170868101]
  Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. We formulate it as a few-shot meta-learning problem where the goal is to find a model that captures the common structure shared across different robotic platforms. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots.
  arXiv  Detail & Related papers  (2021-03-05T14:16:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.