Passive Motion Paradigm: An Alternative to Optimal Control

In the last years, optimal control theory (OCT) has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the “degrees of freedom (DoFs) problem,” the common core of production, observation, reasoning, and learning of “actions.” OCT, directly derived from engineering design techniques of control systems quantifies task goals as “cost functions” and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative “softer” approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that “animates” the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints “at runtime,” hence solving the “DoFs problem” without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of “potential actions.” In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures

The body schema: neural simulation for covert and overt actions of embodied cognitive agents

This brief commentary on the general topic of ‘body schema’ is focused on its computational role, as an internal model that integrates proprioceptive information, for allowing embodied cognitive agents to carry out the neural simulation of covert and overt actions in a unitary manner. The discussion takes inspiration from the vintage but still valid seminal observation by Marr and Poggio that, in order to understand cognitive agents, both human and artificial, we should consider them as Generalized Information Processing Systems, to be analyzed along three levels: computational, algorithmic, and implementation. Accordingly, the body schema concept is briefly analyzed along this line, with the purpose of outlining a cognitive architecture that links embodied cognition with motor control through the body schema

Using the Functional Reach Test for Probing the Static Stability of Bipedal Standing in Humanoid Robots Based on the Passive Motion Paradigm

The goal of this paper is to analyze the static stability of a computational architecture, based on the Passive Motion Paradigm, for coordinating the redundant degrees of freedom of a humanoid robot during whole-body reaching movements in bipedal standing. The analysis is based on a simulation study that implements the Functional Reach Test, originally developed for assessing the danger of falling in elderly people. The study is carried out in the YARP environment that allows realistic simulations with the iCub humanoid robot

Dimension fitting of wheat spikes in dense 3D point clouds based on the adaptive k-means algorithm with dynamic perspectives

The use of dense 3D point clouds to obtain agricultural crop dimensions in the place of manual measurement is crucial for enabling high-throughput phenotyping. To achieve this goal, this paper proposes an adaptive k-means algorithm based on dynamic perspectives, which first performs segmentation in order to separate the wheat spikes. We also propose a method to fit the shape of each spike and measures the dimensions of each spike with the help of the Random Sample Consensus algorithm. The experimental results show that the proposed method can be applied in a complex environment where multiple wheat spikes are grown densely and that it can fit the size of most wheat spikes accurately

A Neural Framework for Organization and Flexible Utilization of Episodic Memory in Cumulatively Learning Baby Humanoids

Cumulatively developing robots offer a unique opportunity to reenact the constant interplay between neural mechanisms related to learning, memory, prospection, and abstraction from the perspective of an integrated system that acts, learns, remembers, reasons, and makes mistakes. Situated within such interplay lie some of the computationally elusive and fundamental aspects of cognitive behavior: the ability to recall and flexibly exploit diverse experiences of one’s past in the context of the present to realize goals, simulate the future, and keep learning further. This article is an adventurous exploration in this direction using a simple engaging scenario of how the humanoid iCub learns to construct the tallest possible stack given an arbitrary set of objects to play with. The learning takes place cumulatively, with the robot interacting with different objects (some previously experienced, some novel) in an open-ended fashion. Since the solution itself depends on what objects are available in the “now,” multiple episodes of past experiences have to be remembered and creatively integrated in the context of the present to be successful. Starting from zero, where the robot knows nothing, we explore the computational basis of organization episodic memory in a cumulatively learning humanoid and address (1) how relevant past experiences can be reconstructed based on the present context, (2) how multiple stored episodic memories compete to survive in the neural space and not be forgotten, (3) how remembered past experiences can be combined with explorative actions to learn something new, and (4) how multiple remembered experiences can be recombined to generate novel behaviors (without exploration). Through the resulting behaviors of the robot as it builds, breaks, learns, and remembers, we emphasize that mechanisms of episodic memory are fundamental design features necessary to enable the survival of autonomous robots in a real world where neither everything can be known nor can everything be experienced

Increased IL-12 and decreased IL-33 serum levels are associated with increased Th1 and suppressed Th2 cytokine profile in patients with diabetic nephropathy (CURES-134).

The role played by recently discovered novel cytokine IL-33 in controlling T-helper (Th)1 and Th2 cytokines under conditions of diabetic nephropathy (DN) is less well studied. In the present study, we estimated the levels of IL-33 along with both Th1 and Th2 cytokines in the serum of normal glucose tolerant (NGT), diabetic subjects with (DN) or without nephropathy (DM) and correlated it with the clinical risk factors of diabetes and nephropathy. 222 study subjects were recruited from the Chennai Urban Rural Epidemiology Study (CURES): 61 NGT, 79 DM and 82 DN. IL-33 level was estimated by ELISA while other Th1 (IL-12, IFN-gamma and IL-2) and Th2 (IL-4, IL-5 and IL-13) cytokines were measured using a Bio-plex bead assay. DM subjects showed a mixed Th1-Th2 profile (increased IFN-g, IL-12, IL-4 and IL-13 and decreased IL-33) while DN subjects showed enhanced Th1 profile (increased IFN-g, IL-2 and IL-12) with suppression of Th2 cytokine (decreased IL-33 and IL-13). The IL-33 levels showed a serial decline with increasing severity of insulin resistance and microalbuminuria. DN was associated with enhanced Th1 response and suppression of Th2 responses which might be due to inreased levels of IL-12 and decreased levels of IL-33 cytokines respectively