Robot navigation and manipulation based on a predictive associative memory

Jockel, Sascha; Mendes, Mateus; Zhang, Jianwei; Coimbra, A. Paulo; Crisóstomo, Manuel

doi:10.1109/devlrn.2009.5175519

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…HD Computing has been applied in different domains, such as visual character recognition [3], cognitive software agents [14], robotics [5], biosignal processing [10], and sequence prediction [1,13].…”

Section: Hyperdimensional Computingmentioning

confidence: 99%

Sequence Prediction with Hyperdimensional Computing

Mercado¹,

Fernández²,

Salinas³

2017

RCS

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Hyperdimensional Computing is an emergent model of computation where all objects are represented in high-dimensional vectors. This model includes a well-defined set of arithmetic operations that produce new highdimensional vectors, which, in addition to represent basic entities, can also represent more complex data structures such as sets, relations and sequences. This paper presents a method for sequence prediction using Hyperdimensional Computing and the Sparse Distributed Memory model. The proposed method is based on the encoding, storage and retrieval of sequence vectors, which store the k consecutive vectors of a sequence. The next element of a sequence is selected by taking into account the current, as well as the k immediate preceding elements of the sequence. Each vector is associated to a sequence vector that is stored in memory; the way in which each vector is associated to its sequence vectors is the main contribution of this paper. We present experimental results for the encoding and prediction of randomly generated sequences and the results indicate that the method performs correct predictions.

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Section: Hyperdimensional Computingmentioning

confidence: 99%

Sequence Prediction with Hyperdimensional Computing

Mercado¹,

Fernández²,

Salinas³

2017

RCS

View full text Add to dashboard Cite

show abstract

“…The SDM is based on the same idea as VSAs that similar or related concepts are represented by nearby points in a high-dimensional space (Kanerva, 1988;Kanerva, 1993). The SDM have been successfully used in numerous applications since the 1990s, e.g., pattern recognition (Hely, Willshaw, & Hayes, 1997;Meng et al, 2009), predictive analytics (Rogers, 1989;Rogers, 1990), robot navigation (Rao & Fuentes, 1998;Jockel, Mendes, Zhang, Coimbra, & Crisostomo, 2009), approximation of Bayesian inference in a fashion similar to Monte Carlo importance sampling (Anderson, 1989;Abbott, Hamrick, & Griffiths, 2013), and biologically inspired cognitive architectures (Rachkovskij, Kussul, & Baidyk, 2013;Franklin, Madl, D'Mello, & Snaider, 2014).…”

Section: Long-term Memorymentioning

confidence: 99%

Vector space architecture for emergent interoperability of systems by learning from demonstration

Emruli

Sandin

Delsing

2015

Biologically Inspired Cognitive Architectures

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The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems automatically interoperable using handcrafted ''semantic'' descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration. ª 2015 Published by Elsevier B.V.

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“…The SDM is based on the same idea as VSAs that similar or related concepts are represented by nearby points in a high-dimensional space (Kanerva, 1988(Kanerva, , 1993. The SDM have been successfully used in numerous applications since the 1990s, e.g., pattern recognition (Hely et al, 1997;Meng et al, 2009), predictive analytics (Rogers, 1989(Rogers, , 1990, robot navigation (Rao and Fuentes, 1998;Jockel et al, 2009), approximation of Bayesian inference in a fashion similar to Monte Carlo importance sampling (Anderson, 1989;Abbott et al, 2013), and biologically inspired cognitive architectures (Rachkovskij et al, 2013;Franklin et al, 2014).…”

Section: Long-term Memorymentioning

confidence: 99%

Vector space architecture for emergent interoperability of systems by learning from demonstration

Emruli

Sandin

Delsing

2014

Biologically Inspired Cognitive Architectures

View full text Add to dashboard Cite

The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems interoperable using handcrafted "semantic" descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (VSA) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration.

show abstract

Robot navigation and manipulation based on a predictive associative memory

Cited by 11 publications

References 13 publications

Sequence Prediction with Hyperdimensional Computing

Sequence Prediction with Hyperdimensional Computing

Vector space architecture for emergent interoperability of systems by learning from demonstration

Vector space architecture for emergent interoperability of systems by learning from demonstration

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