A Unified Approach to Variational Derivatives of Modified Gravitational Actions

Our main aim in this paper is to promote the coframe variational method as a unified approach to derive field equations for any given gravitational action containing the algebraic functions of the scalars constructed from the Riemann curvature tensor and its contractions. We are able to derive a master equation which expresses the variational derivatives of the generalized gravitational actions in terms of the variational derivatives of its constituent curvature scalars. Using the Lagrange multiplier method relative to an orthonormal coframe, we investigate the variational procedures for modified gravitational Lagrangian densities in spacetime dimensions $n\geqslant 3$. We study well-known gravitational actions such as those involving the Gauss-Bonnet and Ricci-squared, Kretchmann scalar, Weyl-squared terms and their algebraic generalizations similar to generic $f(R)$ theories and the algebraic generalization of sixth order gravitational Lagrangians. We put forth a new model involving the gravitational Chern-Simons term and also give three dimensional New massive gravity equations in a new form in terms of the Cotton 2-form

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms

Programa Suriname, agricultura y cambio climático

Locutores: Susana Fevrier y Luis Diego SolorzanoLluvias torrenciales, sequías prolongadas, salinización de las aguas, pérdida de tierras costeras, son algunos de los efectos del cambio climático que afectan la agricultura de Surinam. Un proyecto de tecnologías agrícolas climáticamente inteligentes permite que la producción hortícola de pequeña y mediana escala pueda enfrentar la situación

Programa el pueblo Saamaka: su arroz y el SRI como impulsor de desarrollo

Locutores: Susana Fevrier y Luis Diego Solorzano. Comentarista: Curt DelelisLas mujeres africanas que fueron llevadas a Surinam durante la época de la esclavitud traían consigo una enorme riqueza cultural y, escondidas en el tranzado de su cabello, las semillas de arroz que hicieron posible la vida en libertad de los cimarrones. Hoy la tribu Saamaka, fiel a sus tradiciones, sigue cultivando aquellas variedades, pero el cambio climático y otros factores afectan la productividad y ponen en riesgo su seguridad alimentaria. A través de la metodología de producción SRI se busca revertir esta situación y se impulsa un proceso integral de desarrollo del pueblo cimarrón