Cognitive Models as Bridge between Brain and Behavior

April 20, 2016 16:40 ,

Bradley C. Love, Cognitive Models as Bridge between Brain and Behavior, Trends in Cognitive Sciences, Volume 20, Issue 4, April 2016, Pages 247-248, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.02.006.

How can disparate neural and behavioral measures be integrated? Turner and colleagues propose joint modeling as a solution. Joint modeling mutually constrains the interpretation of brain and behavioral measures by exploiting their covariation structure. Simultaneous estimation allows for more accurate prediction than would be possible by considering these measures in isolation.

Posted in: Cognitive sciences , Tagged:

Integrating humans and robots in the factories

April 20, 2016 16:30 ,

Andrea Cherubini, Robin Passama, André Crosnier, Antoine Lasnier, Philippe Fraisse, Collaborative manufacturing with physical human–robot interaction, Robotics and Computer-Integrated Manufacturing, Volume 40, August 2016, Pages 1-13, ISSN 0736-5845, DOI: 10.1016/j.rcim.2015.12.007.

Although the concept of industrial cobots dates back to 1999, most present day hybrid human–machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human–robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

Posted in: Industrial robots , Tagged:

Incremental (hierarchical) search for the optimal policy on markov decision processes

April 20, 2016 16:26 ,

Vu Anh Huynh, Sertac Karaman, and Emilio Frazzoli, An incremental sampling-based algorithm for stochastic optimal control, The International Journal of Robotics Research April 2016 35: 305-333, DOI: 10.1177/0278364915616866.

In this paper, we consider a class of continuous-time, continuous-space stochastic optimal control problems. Using the Markov chain approximation method and recent advances in sampling-based algorithms for deterministic path planning, we propose a novel algorithm called the incremental Markov Decision Process to incrementally compute control policies that approximate arbitrarily well an optimal policy in terms of the expected cost. The main idea behind the algorithm is to generate a sequence of finite discretizations of the original problem through random sampling of the state space. At each iteration, the discretized problem is a Markov Decision Process that serves as an incrementally refined model of the original problem. We show that with probability one, (i) the sequence of the optimal value functions for each of the discretized problems converges uniformly to the optimal value function of the original stochastic optimal control problem, and (ii) the original optimal value function can be computed efficiently in an incremental manner using asynchronous value iterations. Thus, the proposed algorithm provides an anytime approach to the computation of optimal control policies of the continuous problem. The effectiveness of the proposed approach is demonstrated on motion planning and control problems in cluttered environments in the presence of process noise.

Posted in: Control Engineering , Tagged:

The diverse roles of the hippocampus

April 20, 2016 16:16 ,

Daniel Bendor, Hugo J. Spiers, Does the Hippocampus Map Out the Future?, Trends in Cognitive Sciences, Volume 20, Issue 3, March 2016, Pages 167-169, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.01.003.

Decades of research have established two central roles of the hippocampus – memory consolidation and spatial navigation. Recently, a third function of the hippocampus has been proposed: simulating future events. However, claims that the neural patterns underlying simulation occur without prior experience have come under fire in light of newly published data.

Posted in: Psycho-physiological bases of engineering , Tagged:

Very interesting survey on visual place recognition, including historical background, physio-psychological bases and a definition of “place” in robotics

April 20, 2016 15:45 ,

S. Lowry et al., Visual Place Recognition: A Survey, in IEEE Transactions on Robotics, vol. 32, no. 1, pp. 1-19, Feb. 2016. DOI: 10.1109/TRO.2015.2496823.

Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines-particularly recognition in computer vision and animal navigation in neuroscience-have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition-the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.

Posted in: Mobile robot SLAM , Tagged: ,

Incorporating spatial info into the symbolic (bag-of-words) info used for loop closure detection

April 20, 2016 15:34 ,

Nishant Kejriwal, Swagat Kumar, Tomohiro Shibata, High performance loop closure detection using bag of word pairs, Robotics and Autonomous Systems, Volume 77, March 2016, Pages 55-65, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.12.003.

In this paper, we look into the problem of loop closure detection in topological mapping. The bag of words (BoW) is a popular approach which is fast and easy to implement, but suffers from perceptual aliasing, primarily due to vector quantization. We propose to overcome this limitation by incorporating the spatial co-occurrence information directly into the dictionary itself. This is done by creating an additional dictionary comprising of word pairs, which are formed by using a spatial neighborhood defined based on the scale size of each point feature. Since the word pairs are defined relative to the spatial location of each point feature, they exhibit a directional attribute which is a new finding made in this paper. The proposed approach, called bag of word pairs (BoWP), uses relative spatial co-occurrence of words to overcome the limitations of the conventional BoW methods. Unlike previous methods that use spatial arrangement only as a verification step, the proposed method incorporates spatial information directly into the detection level and thus, influences all stages of decision making. The proposed BoWP method is implemented in an on-line fashion by incorporating some of the popular concepts such as, K-D tree for storing and searching features, Bayesian probabilistic framework for making decisions on loop closures, incremental creation of dictionary and using RANSAC for confirming loop closure for the top candidate. Unlike previous methods, an incremental version of K-D tree implementation is used which prevents rebuilding of tree for every incoming image, thereby reducing the per image computation time considerably. Through experiments on standard datasets it is shown that the proposed methods provide better recall performance than most of the existing methods. This improvement is achieved without making use any geometric information obtained from range sensors or robot odometry. The computational requirements for the algorithm is comparable to that of BoW methods and is shown to be less than the latest state-of-the-art method in this category.

Posted in: Mobile robot SLAM , Tagged: ,

Implementation of spatial relations in graph-SLAM through quaternions instead of homogeneous matrices

April 20, 2016 15:31 ,

Jiantong Cheng, Jonghyuk Kim, Zhenyu Jiang, Wanfang Che, Dual quaternion-based graphical SLAM, Robotics and Autonomous Systems, Volume 77, March 2016, Pages 15-24, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.12.001.

This paper presents a new parameterization approach for the graph-based SLAM problem and reveals the differences of two popular over-parameterized ways in the optimization procedure. In the SALM problem, constraints or relative transformations between any two poses are generally separated into translations plus 3D rotations, which are then described in a homogeneous transformation matrix (HTM) to simplify computational operations. This however introduces added complexities in frequent conversions between the HTM and state variables, due to their different representations. This new approach, unit dual quaternion (UDQ), describes a spatial transformation as a screw with only 8 elements. We show that state variables can be directly represented by UDQs, and how their relative transformations can be written with the UDQ product, without the trivial computations of HTM. Then, we explore the performances of the unit quaternion and the axis–angle representations in the graph-based SLAM problem, which have been successfully applied to over parameterize perturbations under the assumption of small errors. Based on public synthetic and real-world datasets in 2D and 3D environments, experimental results show that the proposed approach reduces greatly the computational complexity while obtaining the same optimization accuracies as the HTM-based algorithm, and the axis–angle representation is superior to be the quaternion in the case of poor initial estimations.

Posted in: Mobile robot SLAM , Tagged: ,

How to make that a symbol becomes related to things on which it is not grounded, and a nice introduction to the symbolist/subsymbolist dilemma

April 20, 2016 15:22 ,

Veale, Tony and Al-Najjar, Khalid (2016). Grounded for life: creative symbol-grounding for lexical invention. Connection Science 28(2). DOI: 10.1080/09540091.2015.1130025

One of the challenges of linguistic creativity is to use words in a way that is novel and striking and even whimsical, to convey meanings that remain stubbornly grounded in the very same world of familiar experiences as serves to anchor the most literal and unimaginative language. The challenge remains unmet by systems that merely shuttle or arrange words to achieve novel arrangements without concern as to how those arrangements are to spur the processes of meaning construction in a listener. In this paper we explore a problem of lexical invention that cannot be solved without a model ? explicit or implicit ? of the perceptual grounding of language: the invention of apt new names for colours. To solve this problem here we shall call upon the notion of a linguistic readymade, a phrase that is wrenched from its original context of use to be given new meaning and new resonance in new settings. To ensure that our linguistic readymades ? which owe a great deal to Marcel Duchamp’s notion of found art ? are anchored in a consensus model of perception, we introduce the notion of a lexicalised colour stereotype.

Posted in: Artificial Intelligence , Tagged: , ,

Using multiple RANSACs for tracking

February 24, 2016 19:13 ,

Peter C. Niedfeldt and Randal W. Beard, Convergence and Complexity Analysis of Recursive-RANSAC: A New Multiple Target Tracking Algorithm, in IEEE Transactions on Automatic Control , vol.61, no.2, pp.456-461, Feb. 2016, DOI: 10.1109/TAC.2015.2437518.

The random sample consensus (RANSAC) algorithm was developed as a regression algorithm that robustly estimates the parameters of a single signal in clutter by forming many simple hypotheses and computing how many measurements support that hypothesis. In essence, RANSAC estimates the data association problem of a single target in clutter by identifying the hypothesis with the most supporting measurements. The newly developed recursive-RANSAC (R-RANSAC) algorithm extends the traditional RANSAC algorithm to track multiple targets recursively by storing a set of hypotheses between time steps. In this technical note we show that R-RANSAC converges to the minimum mean-squared solution for well-spaced targets. We also show that the worst-case computational complexity of R-RANSAC is quadratic in the number of new measurements and stored models.

Posted in: Mathematics , Tagged: ,

Limitations of the simulation of physical systems when used in AI reasoning processes for prediction

February 24, 2016 19:09 ,

Ernest Davis, Gary Marcus, The scope and limits of simulation in automated reasoning, Artificial Intelligence, Volume 233, April 2016, Pages 60-72, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.12.003.

In scientific computing and in realistic graphic animation, simulation – that is, step-by-step calculation of the complete trajectory of a physical system – is one of the most common and important modes of calculation. In this article, we address the scope and limits of the use of simulation, with respect to AI tasks that involve high-level physical reasoning. We argue that, in many cases, simulation can play at most a limited role. Simulation is most effective when the task is prediction, when complete information is available, when a reasonably high quality theory is available, and when the range of scales involved, both temporal and spatial, is not extreme. When these conditions do not hold, simulation is less effective or entirely inappropriate. We discuss twelve features of physical reasoning problems that pose challenges for simulation-based reasoning. We briefly survey alternative techniques for physical reasoning that do not rely on simulation.

Posted in: Artificial Intelligence , Tagged: ,