=Paper=
{{Paper
|id=Vol-1855/EUCognition_2016_Part15
|storemode=property
|title=Artificial Spatial Cognition for Robotics and Mobile Systems: Brief Survey and Current Open Challenges
|pdfUrl=https://ceur-ws.org/Vol-1855/EUCognition_2016_Part15.pdf
|volume=Vol-1855
|authors=Paloma de la Puente,M. Guadalupe Sánchez-Escribano
|dblpUrl=https://dblp.org/rec/conf/eucognition/PuenteS16
}}
==Artificial Spatial Cognition for Robotics and Mobile Systems: Brief Survey and Current Open Challenges ==
https://ceur-ws.org/Vol-1855/EUCognition_2016_Part15.pdf
Artificial Spatial Cognition for Robotics and Mobile
Systems: Brief Survey and Current Open Challenges
Paloma de la Puente and M. Guadalupe
Sánchez-Escribano Universidad Politécnica de
Madrid (UPM) Madrid, Spain
Abstract—Remarkable and impressive advancements in the modeling general intelligent agents and their main
areas of perception, mapping and navigation of artificial mobile commitments support the ambitious requirements of high level
systems have been witnessed in the last decades. However, it is behavior in arbitrary situations for robotics [10]. A more recent
clear that important limitations remain regarding the spatial model of spatial knowledge, the Spatial/Visual System (SVS)
cognition capabilities of existing available implementations and [11] designed as an extension of the Soar cognitive
the current practical functionality of high level cognitive models architecture, proposed a different multiplicity of
[1, 2]. For enhanced robustness and flexibility in different kinds representations, i.e. symbolic, quantitative spatial and visual
of real world scenarios, a deeper understanding of the depictive. The spatial scene is a hierarchy tree of entities and
environment, the system, and their interactions -in general terms-
their constitutive parts, with intermediate nodes defining the
is desired. This long abstract aims at outlining connections
transformation relations between parts and objects. Other
between recent contributions in the above mentioned areas and
research in cognitive architectures and biological systems. We try works in robotics employ similar internal representation ideas
to summarize, integrate and update previous reviews, [12-14], and other ones included the possibility to hypothesize
highlighting the main open issues and aspects not yet unified or geometric environment structure in order to build consistent
integrated in a common architectural framework. maps [15]. While a complete implementation of this approach
for all kind of objects requires solving the corresponding
Keywords—spatial cognition; surveys; perception; navigation segmentation and recognition problems in a domain
independent manner (which is far beyond the state of the art),
I. BRIEF SURVEY keeping the perceptual level representations within the
architecture enhances functionality. A very active research
A. Initial models for spatial knowledge representation and community address these difficult challenges.
main missing elements The recognition process should not only use visual, spatial
Focusing on the spatial knowledge representation and and motion data from the Perceptual LTM but also conceptual
management, the first contributions inspired by the human context information [7, 16] and episodic memories of
cognitive map combined metric local maps, as an Absolute remembered places [17], from Symbolic LTM. This should
Space Representation (ASR), and topological graphs [3]. As a also apply to the navigation techniques for different situations
related approach, the Spatial Semantic Hierarchy (SSH) [4] [18, 19]. The existence of motion models for the objects can
was the first fundamental cognitive model for large-scale improve navigation in dynamic environments, which is one of
space. It evolved into the Hybrid SSH [5], which also included the main problems in real world robotic applications [20, 21].
knowledge about small-scale space. This fundamental work A novel cognitive architecture specifically designed for
was undoubtedly groundbreaking, but it did not go beyond spatial knowledge processing is the Casimir architecture [22],
basic levels of information abstraction and conceptualization which presents rich modeling capabilities pursuing human-like
[6]. Moreover, the well-motivated dependencies among behavior. Navigation, however, has not been addressed, and
different types of knowledge (both declarative and procedural) this work has scarcely been discussed in the robotics domain.
were not further considered for general problem solving [7].
The SSH model was considered suitable for the popular One of the latest spatial models is the NavModel [23],
schema of a “three layer architecture”, without explicitly designed and implemented for the ACT-R architecture. Besides
dealing with processes such as attention or forgetting considering multi-level representations, this model presents
mechanisms. This lack of principled forgetting mechanisms has three navigation strategies with varying cognitive cost. The
been identified by the Simultaneous Localization and Mapping first developed implementation assumes known topological
(SLAM) robotics community as a key missing feature of most localization at room level, while a subsequent implementation
existing mapping approaches [8, 9]. incorporates a mental rotation model. This work focuses on the
cognitive load and does not deal with lower level issues.
B. The role of cognitive architectures and their relation to To point out how topics are addressed by the respective
other works in the robotics community communities, we compiled Table I as a comparison. The
Cognitive architectures provide a solid approach for contrast regarding memory management and uncertainty seems
This work was partially funded by the Spanish Ministry of Economics and
Competitivity: DPI 2014-53525-C3-1-R, NAVEGASE. It also received funding
from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la
calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), supported by
Programas de Actividades I+D en la Comunidad de Madrid and co-founded
by Structural Funds of the EU.
Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 52
to be relevant. The lack of approaches combining both [2] T. Madl, K. Chen, D. Montaldi and R. Trappl. Computational cognitive
allocentric and egocentric representations is also remarkable. models of spatial memory in navigation space: A review. Neural
Networks, 2015.
To conclude, Table II shows a summary of surveys.
[3] W.K. Yeap. Towards a computational theory of cognitive maps. Journal
of Artificial Intelligence, 1988.
TABLE I. COMPARISON OF TOPICS ADDRESSED BY THE COGNITIVE [4] B. Kuipers. The spatial semantic hierarchy. Artificial Intelligence. 2000.
ARCHITECTURES AND ROBOTICS COMMUNITIES [5] Kuipers, J. Modayil, P. Beeson, M.MacMahon and F. Savelli. Local
Cognitive Architectures ← Topic → Perception, Robotics,
metrical and global topological maps in the hybrid Spatial Semantic
Community Vehicles Community Hierarchy. ICRA, 2004.
ACT-R/S, CLARION Egocentric spatial models [24, 25] [6] A. Pronobis and P. Jensfelt. Large-scale semantic mapping and
reasoning with heterogeneous modalities. ICRA, 2012.
LIDA, SOAR-SVS Allocentric spatial models [9, 26]
[7] S.D. Lathrop. Extending cognitive architectures with spatial and visual
Casimir, LIDA, SOAR-SVS Object based/ semantic representations [6, 12-14] imagery mechanisms. PhD Thesis, 2008.
SOAR-SVS
Explicit motion models / dynamic
[27, 28] [8] J.A. Fernandez-Madrigal and J.L. Blanco. Simultaneous localization and
information about the environment
mapping for mobile robots: iIntroduction and methods. IGI, 2012.
Memory management, forgetting [9] C. Cadena et al. Past, present, and future of simultaneous localization
All [19]
mechanisms
and mapping: towards the robust-perception age. T-RO, 2016.
Most mapping and
Extended LIDA [29] Uncertainty considerations
navigation approaches
[10] U. Kurup and C. Lebiere. What can cognitive architectures do for
robotics? Biologically Inspired Cognitive Architectures, 2012.
[11] S.D. Lathrop. Exploring the functional advantages of spatial and visual
cognition from an architectural perspective. TopiCS 2011.
TABLE II. SUMMARY OF SURVEYS [12] R.F. Salas-Moreno, R.A: Newcombe, H. Strasdat, P.H.J Kelly and A.J.
Davison. SLAM++: Simultaneous localisation and mapping at the Level
Topic References of objects. CVPR, 2013.
Robotics and Cognitive Mapping [1] [13] S. Eslami and C. Williams. A generative model for parts-based object
segmentation. Advances Neural Information Processing Systems, 2012.
SLAM and Robust Perception [8, 9]
[14] A. Uckermann, C. Eibrechter, R. Haschke and H. Ritter. Real time
Computational cognitive models
of spatial memory
[2] hierarchical scene segmentation and classification. Humanoids, 2014.
[15] P. de la Puente and D. Rodriguez-Losada. Feature based graph SLAM in
Object recognition [30, 31]
structured environments. Autonomous Robots, 2014.
Cognitive Architectures for Robotics [10]
[16] L. Kunze et al. Combining top-down spatial reasoning and bottom-up
Spatial knowledge in brains [17] object class recognition for scene understanding. IROS, 2014.
[17] M.B Moser and E.I. Moser. The brain's GPS. Scientific American, 2016.
[18] G. Gunzelmann and D. Lyon (2007) Mechanisms for human spatial
competence. Spatial Cognition V, LNAI-Springer, 2007.
II. CURRENT OPEN CHALLENGES
[19] F. Dayoub, G. Cielniak and T. Duckett. Eight weeks of episodic visual
The big challenge is closing the gap between high level navigation inside a non-stationary environment using adaptive spherical
models and actual implementations in artificial mobile systems. views. FSR, 2013.
To reduce this existing gap, we identify three main goals: [20] N. Hawes et al. The STRANDS project: long-term autonomy in
everyday environments. Robotics and Automation Magazine, in press.
Combination of allocentric and egocentric models [21] P. de la Puente et al. Experiences with RGB-D navigation in real home
using different levels of features/objects + robotic trials. ARW, 2016.
topology/semantics. [22] H. Schultheis and T. Barkowsky. Casimir: an architecture for mental
spatial knowledge processing. TopiCS, 2011.
Acquisition and integration of motion models and
[23] C. Zhao. Understanding human spatial navigation behaviors: A
dynamic information for the elements/objects. cognitive modeling. PhD Thesis, 2016.
Integration of global mapping & loop closure [24] R. Drouilly, P. Rives and B. Morisset. Semantic representation for
capabilities with extensive declarative knowledge navigation in large-scale environments. ICRA, 2015.
about features relevance and forgetting [25] L.F. Posada, F. Hoffmann and T. Bertram. Visual semantic robot
mechanisms with episodic memory. navigation in indoor environments. ISR, 2014.
[26] A. Richardson and E. Olson. Iterative path optimization for practical
robot planning. IROS, 2011.
ACKNOWLEDGMENT
[27] R. Ambrus, N. Bore, J. Folkesson and P. Jensfelt. Meta-rooms: building
The authors want to thank the EUCog community for and maintaining long term spatial models in a dynamic world. IROS,
fostering interdisciplinary research in Artificial Cognitive 2014.
Systems and organizing inspiring meetings and events. [28] D. M. Rosen, J. Mason and J. J. Leonard. Towards lifelong feature-
based mapping in semi-static environments. ICRA, 2016.
[29] T. Madl, S. Franklin, K. Chen, D. Montaldi and R. Trappl. Towards real-
world capable spatial memory in the LIDA cognitive architecture.
REFERENCES BICA, 2016.
[30] J. J. DiCarlo, D. Zoccolan and N. C. Rust. How does the brain solve
[1] G. Eason M. Jefferies and W.K. Yeap. Robotics and cognitive visual object recognition? Neuron, 2012.
approaches to spatial mapping. Springer, 2008.
Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 53