1
|
Width-Based Search for Multi Agent Privacy-Preserving Planning. ARTIF INTELL 2023. [DOI: 10.1016/j.artint.2023.103883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
|
2
|
Meli D, Nakawala H, Fiorini P. Logic programming for deliberative robotic task planning. Artif Intell Rev 2023. [DOI: 10.1007/s10462-022-10389-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
AbstractOver the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application.
Collapse
|
3
|
Belle V, Bolander T, Herzig A, Nebel B. Epistemic planning: Perspectives on the special issue. ARTIF INTELL 2022. [DOI: 10.1016/j.artint.2022.103842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
4
|
Thomason W, Strub MP, Gammell JD. Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3199676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Wil Thomason
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Marlin P. Strub
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jonathan D. Gammell
- Estimation, Search, and Planning (ESP) Group, Oxford Robotics Institute, University of Oxford, Oxford, U.K
| |
Collapse
|
5
|
Lee J, Rakhman U, Nam C, Kang S, Park J, Kim C. High dimensional object rearrangement for a robot manipulation in highly dense configurations. INTEL SERV ROBOT 2022. [DOI: 10.1007/s11370-022-00444-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
6
|
Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters. ARTIF INTELL 2022. [DOI: 10.1016/j.artint.2022.103789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
7
|
Jeon J, Jung HR, Luong T, Yumbla F, Moon H. Combined task and motion planning system for the service robot using hierarchical action decomposition. INTEL SERV ROBOT 2022. [DOI: 10.1007/s11370-022-00437-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
8
|
Moerland TM, Broekens J, Plaat A, Jonker CM. A Unifying Framework for Reinforcement Learning and Planning. Front Artif Intell 2022; 5:908353. [PMID: 35898393 PMCID: PMC9309375 DOI: 10.3389/frai.2022.908353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/06/2022] [Indexed: 11/24/2022] Open
Abstract
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem, then we might be able to disentangle the common factors in their solution approaches. Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning and learning algorithms have to decide. At the end of the paper, we compare a variety of well-known planning, model-free and model-based RL algorithms along these dimensions. Altogether, the framework may help provide deeper insight in the algorithmic design space of planning and reinforcement learning.
Collapse
Affiliation(s)
- Thomas M. Moerland
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, Netherlands
- *Correspondence: Thomas M. Moerland
| | - Joost Broekens
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, Netherlands
| | - Aske Plaat
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, Netherlands
| | - Catholijn M. Jonker
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, Netherlands
- Interactive Intelligence, Delft University of Technology, Delft, Netherlands
| |
Collapse
|
9
|
Moreira LH, Ralha CG. Method for evaluating plan recovery strategies in dynamic multi-agent environments. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2078887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Leonardo Henrique Moreira
- Department of Computer Science, Institute of Exact Sciences University of Brasília, Brasília-DF, Brazil
| | - Célia Ghedini Ralha
- Department of Computer Science, Institute of Exact Sciences University of Brasília, Brasília-DF, Brazil
| |
Collapse
|
10
|
Pira E. Using deep learning techniques for solving AI planning problems specified through graph transformations. Soft comput 2022. [DOI: 10.1007/s00500-022-07044-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
11
|
An efficient lightweight coordination model to multi-agent planning. Knowl Inf Syst 2022. [DOI: 10.1007/s10115-021-01638-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
12
|
Percassi F, Gerevini AE, Scala E, Serina I, Vallati M. Improving Domain-Independent Heuristic State-Space Planning via plan cost predictions. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2021.1970239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Francesco Percassi
- Dipartimento d’Ingegneria dell’Informazione, Universitá Degli Studi Di Brescia, Brescia, Italy
- Department of Informatics, University of Huddersfield, Huddersfield, UK
| | - Alfonso E. Gerevini
- Dipartimento d’Ingegneria dell’Informazione, Universitá Degli Studi Di Brescia, Brescia, Italy
| | - Enrico Scala
- Dipartimento d’Ingegneria dell’Informazione, Universitá Degli Studi Di Brescia, Brescia, Italy
| | - Ivan Serina
- Dipartimento d’Ingegneria dell’Informazione, Universitá Degli Studi Di Brescia, Brescia, Italy
| | - Mauro Vallati
- Department of Informatics, University of Huddersfield, Huddersfield, UK
| |
Collapse
|
13
|
Vilchis-Medina JL, Godary-Dejean K, Lesire C. Autonomous Decision-Making With Incomplete Information and Safety Rules Based on Non-Monotonic Reasoning. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3103048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
14
|
Kim B, Shimanuki L, Kaelbling LP, Lozano-Pérez T. Representation, learning, and planning algorithms for geometric task and motion planning. Int J Rob Res 2021. [DOI: 10.1177/02783649211038280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We present a framework for learning to guide geometric task-and-motion planning (G-TAMP). G-TAMP is a subclass of task-and-motion planning in which the goal is to move multiple objects to target regions among movable obstacles. A standard graph search algorithm is not directly applicable, because G-TAMP problems involve hybrid search spaces and expensive action feasibility checks. To handle this, we introduce a novel planner that extends basic heuristic search with random sampling and a heuristic function that prioritizes feasibility checking on promising state–action pairs. The main drawback of such pure planners is that they lack the ability to learn from planning experience to improve their efficiency. We propose two learning algorithms to address this. The first is an algorithm for learning a rank function that guides the discrete task-level search, and the second is an algorithm for learning a sampler that guides the continuous motion-level search. We propose design principles for designing data-efficient algorithms for learning from planning experience and representations for effective generalization. We evaluate our framework in challenging G-TAMP problems, and show that we can improve both planning and data efficiency.
Collapse
Affiliation(s)
- Beomjoon Kim
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | | | - Leslie Pack Kaelbling
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tomás Lozano-Pérez
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
15
|
Effective grounding for hybrid planning problems represented in PDDL+. KNOWL ENG REV 2021. [DOI: 10.1017/s0269888921000072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract
Automated planning is the field of Artificial Intelligence (AI) that focuses on identifying sequences of actions allowing to reach a goal state from a given initial state. The need of using such techniques in real-world applications has brought popular languages for expressing automated planning problems to provide direct support for continuous and discrete state variables, along with changes that can be either instantaneous or durative. PDDL+ (Planning Domain Definition Language +) models support the encoding of such representations, but the resulting planning problems are notoriously difficult for AI planners to cope with due to non-linear dependencies arising from the variables and infinite search spaces. This difficulty is exacerbated by the potentially huge fully ground representations used by modern planners in order to effectively explore the search space, which can make some problems impossible to tackle.
This paper investigates two grounding techniques for PDDL+ problems, both aimed at reducing the size of the full ground representation by reasoning over the lifted, more abstract problem structure. The first method extends the simple mechanism of invariant analysis to limit the groundings of operators upfront. The second method proposes to tackle the grounding process through a PDDL+ to classical planning abstraction; this allows us to leverage the amount of research done in the classical planning area. Our empirical analysis studies the effect of these novel approaches over both real-world hybrid applications and synthetic PDDL+ problems took from standard benchmarks of the planning community; our results reveal that not only the techniques improve the running time of previous grounding mechanisms but also let the planner extend the reach to problems that were not solvable before.
Collapse
|
16
|
Sun C, Kingry N, Dai R. A Unified Formulation and Nonconvex Optimization Method for Mixed-Type Decision-Making of Robotic Systems. IEEE T ROBOT 2021. [DOI: 10.1109/tro.2020.3036619] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
17
|
Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11104324] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.
Collapse
|
18
|
Houssein EH, Mahdy MA, Eldin MG, Shebl D, Mohamed WM, Abdel-Aty M. Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm. J Adv Res 2021; 29:147-157. [PMID: 33842012 PMCID: PMC8020354 DOI: 10.1016/j.jare.2020.10.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/08/2020] [Accepted: 10/11/2020] [Indexed: 11/18/2022] Open
Abstract
Introduction Quantum cloning operation, started with no-go theorem which proved that there is no capability to perform a cloning operation on an unknown quantum state, however, a number of trials proved that we can make approximate quantum state cloning that is still with some errors. Objectives To the best of our knowledge, this paper is the first of its kind to attempt using meta-heuristic algorithm such as Adaptive Guided Differential Evolution (AGDE), to tackle the problem of quantum cloning circuit parameters to enhance the cloning fidelity. Methods To investigate the effectiveness of the AGDE, the extensive experiments have demonstrated that the AGDE can achieve outstanding performance compared to other well-known meta-heuristics including; Enhanced LSHADE-SPACMA Algorithm (ELSHADE-SPACMA), Enhanced Differential Evolution algorithm with novel control parameter adaptation (PaDE), Improved Multi-operator Differential Evolution Algorithm (IMODE), Parameters with adaptive learning mechanism (PALM), QUasi-Affine TRansformation Evolutionary algorithm (QUATRE), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Cuckoo Search (CS), Bat-inspired Algorithm (BA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). Results In the present study, AGDE is applied to improve the fidelity of quantum cloning problem and the obtained parameter values minimize the cloning difference error value down to 10 - 8 . Conclusion Accordingly, the qualitative and quantitative measurements including average, standard deviation, convergence curves of the competitive algorithms over 30 independent runs, proved the superiority of AGDE to enhance the cloning fidelity.
Collapse
Affiliation(s)
- Essam H. Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
- Corresponding author.
| | - Mohamed A. Mahdy
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
| | - Manal. G. Eldin
- Department of Mathematics and Computer Science, Faculty of Science, Beni-Suef University, Beni-Suef, Egypt
| | - Doaa Shebl
- Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
| | - Waleed M. Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Mahmoud Abdel-Aty
- Department of Mathematics, Faculty of Science, Sohag University, Sohag, Egypt
| |
Collapse
|
19
|
iRoPro: An interactive Robot Programming Framework. Int J Soc Robot 2021. [DOI: 10.1007/s12369-021-00775-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
20
|
García-Olaya A, de la Rosa T, Borrajo D. Selecting goals in oversubscription planning using relaxed plans. ARTIF INTELL 2021. [DOI: 10.1016/j.artint.2020.103414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
21
|
Nair L, Chernova S. Feature Guided Search for Creative Problem Solving Through Tool Construction. Front Robot AI 2020; 7:592382. [PMID: 33501352 PMCID: PMC7806064 DOI: 10.3389/frobt.2020.592382] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 12/01/2020] [Indexed: 11/13/2022] Open
Abstract
Robots in the real world should be able to adapt to unforeseen circumstances. Particularly in the context of tool use, robots may not have access to the tools they need for completing a task. In this paper, we focus on the problem of tool construction in the context of task planning. We seek to enable robots to construct replacements for missing tools using available objects, in order to complete the given task. We introduce the Feature Guided Search (FGS) algorithm that enables the application of existing heuristic search approaches in the context of task planning, to perform tool construction efficiently. FGS accounts for physical attributes of objects (e.g., shape, material) during the search for a valid task plan. Our results demonstrate that FGS significantly reduces the search effort over standard heuristic search approaches by ≈93% for tool construction.
Collapse
Affiliation(s)
- Lakshmi Nair
- Georgia Institute of Technology, Atlanta, GA, United States
| | | |
Collapse
|
22
|
Grastien A, Scala E. CPCES: A planning framework to solve conformant planning problems through a counterexample guided refinement. ARTIF INTELL 2020. [DOI: 10.1016/j.artint.2020.103271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
23
|
Luis N, Pereira T, Fernández S, Moreira A, Borrajo D, Veloso M. Using Pre-Computed Knowledge for Goal Allocation in Multi-Agent Planning. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-019-01022-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
24
|
Kattepur A, Purushotaman B. RoboPlanner
: a pragmatic task planning framework for autonomous robots. COGNITIVE COMPUTATION AND SYSTEMS 2020. [DOI: 10.1049/ccs.2019.0025] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Affiliation(s)
- Ajay Kattepur
- Embedded Systems and Robotics, TCS Research & InnovationIndia
| | | |
Collapse
|
25
|
Amato F, Moscato F, Moscato V, Pascale F, Picariello A. An agent-based approach for recommending cultural tours. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.01.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
26
|
Plan merging by reuse for multi-agent planning. APPL INTELL 2020. [DOI: 10.1007/s10489-019-01429-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
27
|
|
28
|
|
29
|
An Abstract Framework for Non-Cooperative Multi-Agent Planning. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9235180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents’ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.
Collapse
|
30
|
|
31
|
|
32
|
Imeson F, Smith SL. An SMT-Based Approach to Motion Planning for Multiple Robots With Complex Constraints. IEEE T ROBOT 2019. [DOI: 10.1109/tro.2019.2896401] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
33
|
Muñoz P, R-Moreno MD, Barrero DF, Ropero F. MoBAr: a Hierarchical Action-Oriented Autonomous Control Architecture. J INTELL ROBOT SYST 2019. [DOI: 10.1007/s10846-018-0810-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
34
|
|
35
|
Chrpa L, Vallati M, McCluskey TL. Inner entanglements: Narrowing the search in classical planning by problem reformulation. Comput Intell 2019. [DOI: 10.1111/coin.12203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Lukáš Chrpa
- Faculty of Electrical EngineeringCzech Technical University in Prague Prague Czech Republic
- Faculty of Mathematics and PhysicsCharles University Prague Czech Republic
| | - Mauro Vallati
- School of Computing and EngineeringUniversity of Huddersfield Huddersfield UK
| | | |
Collapse
|
36
|
PMK-A Knowledge Processing Framework for Autonomous Robotics Perception and Manipulation. SENSORS 2019; 19:s19051166. [PMID: 30866544 PMCID: PMC6427735 DOI: 10.3390/s19051166] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 02/25/2019] [Accepted: 03/01/2019] [Indexed: 11/16/2022]
Abstract
Autonomous indoor service robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions. Particularly, for complex manipulation tasks which are subject to geometric constraints, spatial information and a rich semantic knowledge about objects, types, and functionality are required, together with the way in which these objects can be manipulated. In this line, this paper presents an ontological-based reasoning framework called Perception and Manipulation Knowledge (PMK) that includes: (1) the modeling of the environment in a standardized way to provide common vocabularies for information exchange in human-robot or robot-robot collaboration, (2) a sensory module to perceive the objects in the environment and assert the ontological knowledge, (3) an evaluation-based analysis of the situation of the objects in the environment, in order to enhance the planning of manipulation tasks. The paper describes the concepts and the implementation of PMK, and presents an example demonstrating the range of information the framework can provide for autonomous robots.
Collapse
|
37
|
|
38
|
|
39
|
Wells AM, Dantam NT, Shrivastava A, Kavraki LE. Learning Feasibility for Task and Motion Planning in Tabletop Environments. IEEE Robot Autom Lett 2019; 4:1255-1262. [PMID: 31058229 DOI: 10.1109/lra.2019.2894861] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Task and motion planning (TMP) combines discrete search and continuous motion planning. Earlier work has shown that to efficiently find a task-motion plan, the discrete search can leverage information about the continuous geometry. However, incorporating continuous elements into discrete planners presents challenges. We improve the scalability of TMP algorithms in tabletop scenarios with a fixed robot by introducing geometric knowledge into a constraint-based task planner in a robust way. The key idea is to learn a classifier for feasible motions and to use this classifier as a heuristic to order the search for a task-motion plan. The learned heuristic guides the search towards feasible motions and thus reduces the total number of motion planning attempts. A critical property of our approach is allowing robust planning in diverse scenes. We train the classifier on minimal exemplar scenes and then use principled approximations to apply the classifier to complex scenarios in a way that minimizes the effect of errors. By combining learning with planning, our heuristic yields order-of-magnitude run time improvements in diverse tabletop scenarios. Even when classification errors are present, properly biasing our heuristic ensures we will have little computational penalty.
Collapse
Affiliation(s)
- Andrew M Wells
- School of Engineering, Department of Computer Science, Rice University, Houston TX andrew DOT wells AT rice DOT edu, lastname AT rice DOT edu
| | - Neil T Dantam
- School of Engineering, Department of Computer Science, Colorado School of Mines, Golden CO dantam AT mines DOT edu
| | - Anshumali Shrivastava
- School of Engineering, Department of Computer Science, Rice University, Houston TX andrew DOT wells AT rice DOT edu, lastname AT rice DOT edu
| | - Lydia E Kavraki
- School of Engineering, Department of Computer Science, Rice University, Houston TX andrew DOT wells AT rice DOT edu, lastname AT rice DOT edu
| |
Collapse
|
40
|
Akbari A, Muhayyuddin, Rosell J. Knowledge-oriented task and motion planning for multiple mobile robots. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1544280] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Aliakbar Akbari
- Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Muhayyuddin
- Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Jan Rosell
- Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| |
Collapse
|
41
|
|
42
|
Akbari A, Lagriffoul F, Rosell J. Combined heuristic task and motion planning for bi-manual robots. Auton Robots 2018. [DOI: 10.1007/s10514-018-9817-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
43
|
Garrett CR, Lozano-Pérez T, Kaelbling LP. Sampling-based methods for factored task and motion planning. Int J Rob Res 2018. [DOI: 10.1177/0278364918802962] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing.
Collapse
|
44
|
Chrpa L, Vallati M, McCluskey TL. Outer entanglements: a general heuristic technique for improving the efficiency of planning algorithms. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1509377] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Lukáš Chrpa
- Department of Computer Science, Czech Technical University in Prague, Prague, Czech Republic
- Department of Theoretical Computer Science and Mathematical Logic, Charles University in Prague, Prague, Czech Republic
| | - Mauro Vallati
- Department of Informatics, University of Huddersfield, Queensgate, UK
| | | |
Collapse
|
45
|
Choudhury S, Bhardwaj M, Arora S, Kapoor A, Ranade G, Scherer S, Dey D. Data-driven planning via imitation learning. Int J Rob Res 2018. [DOI: 10.1177/0278364918781001] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths, whereas the objective for an exploration task would be to map unknown areas. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial-information-based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle: an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial-information-based policies: informative path planning and search-based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms. Our framework is able to train policies that achieve up to [Formula: see text] more reward than state-of-the art information-gathering heuristics and a [Formula: see text] speedup as compared with A* on search-based planning problems. Our approach paves the way forward for applying data-driven techniques to other such problem domains under the umbrella of robot planning.
Collapse
|
46
|
|
47
|
Bernardini S, Fagnani F, Smith DE. Extracting mutual exclusion invariants from lifted temporal planning domains. ARTIF INTELL 2018. [DOI: 10.1016/j.artint.2018.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
48
|
What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask). KNOWL ENG REV 2018. [DOI: 10.1017/s0269888918000012] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.
Collapse
|
49
|
Bonisoli A, Emilio Gerevini A, Saetti A, Serina I. A privacy-preserving model for multi-agent propositional planning. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1456786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Andrea Bonisoli
- Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia , Brescia, Italy
| | - Alfonso Emilio Gerevini
- Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia , Brescia, Italy
| | - Alessandro Saetti
- Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia , Brescia, Italy
| | - Ivan Serina
- Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia , Brescia, Italy
| |
Collapse
|
50
|
|