Augmenting Agent Platforms to Facilitate Conversation Reasoning

Within Multi Agent Systems, communication by means of Agent Communication Languages (ACLs) has a key role to play in the co-operation, co-ordination and knowledge-sharing between agents. Despite this, complex reasoning about agent messaging, and specifically about conversations between agents, tends not to have widespread support amongst general-purpose agent programming languages. ACRE (Agent Communication Reasoning Engine) aims to complement the existing logical reasoning capabilities of agent programming languages with the capability of reasoning about complex interaction protocols in order to facilitate conversations between agents. This paper outlines the aims of the ACRE project and gives details of the functioning of a prototype implementation within the Agent Factory multi agent framework

Assessment of simulated and real-world autonomy performance with small-scale unmanned ground vehicles

Off-road autonomy is a challenging topic that requires robust systems to both understand and navigate complex environments. While on-road autonomy has seen a major expansion in recent years in the consumer space, off-road systems are mostly relegated to niche applications. However, these applications can provide safety and navigation to dangerous areas that are the most suited for autonomy tasks. Traversability analysis is at the core of many of the algorithms employed in these topics. In this thesis, a Clearpath Robotics Jackal vehicle is equipped with a 3D Ouster laser scanner to define and traverse off-road environments. The Mississippi State University Autonomous Vehicle Simulator (MAVS) and the Navigating All Terrains Using Robotic Exploration (NATURE) autonomy stack are used in conjunction with the small-scale vehicle platform to traverse uneven terrain and collect data. Additionally, the NATURE stack is used as a point of comparison between a MAVS simulated and physical Clearpath Robotics Jackal vehicle in testing

TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning

As safety is of paramount importance in robotics, reinforcement learning that reflects safety, called safe RL, has been studied extensively. In safe RL, we aim to find a policy which maximizes the desired return while satisfying the defined safety constraints. There are various types of constraints, among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC. We first derive the upper bound on CVaR and then approximate the upper bound in a differentiable form in a trust region. Using this approximation, a subproblem to get policy gradients is formulated, and policies are trained by iteratively solving the subproblem. TRC is evaluated through safe navigation tasks in simulations with various robots and a sim-to-real environment with a Jackal robot from Clearpath. Compared to other safe RL methods, the performance is improved by 1.93 times while the constraints are satisfied in all experiments.Comment: RA-L and ICRA 202

Position-agnostic autonomous navigation in vineyards with Deep Reinforcement Learning

Precision agriculture is rapidly attracting research to efficiently introduce automation and robotics solutions to support agricultural activities. Robotic navigation in vineyards and orchards offers competitive advantages in autonomously monitoring and easily accessing crops for harvesting, spraying and performing time-consuming necessary tasks. Nowadays, autonomous navigation algorithms exploit expensive sensors which also require heavy computational cost for data processing. Nonetheless, vineyard rows represent a challenging outdoor scenario where GPS and Visual Odometry techniques often struggle to provide reliable positioning information. In this work, we combine Edge AI with Deep Reinforcement Learning to propose a cutting-edge lightweight solution to tackle the problem of autonomous vineyard navigation with-out exploiting precise localization data and overcoming task-tailored algorithms with a flexible learning-based approach. We train an end-to-end sensorimotor agent which directly maps noisy depth images and position-agnostic robot state information to velocity commands and guides the robot to the end of a row, continuously adjusting its heading for a collision-free central trajectory. Our extensive experimentation in realistic simulated vineyards demonstrates the effectiveness of our solution and the generalization capabilities of our agent

The role of wild canids and felids in spreading parasites to dogs and cats in Europe. Part II: Helminths and arthropods.

Over the last few decades, ecological factors, combined with everchanging landscapes mainly linked to human activities (e.g. encroachment and tourism) have contributed to modifications in the transmission of parasitic diseases from domestic to wildlife carnivores and vice versa. In the first of this two-part review article, we have provided an account of diseases caused by protozoan parasites characterised by a two-way transmission route between domestic and wild carnivore species. In this second and final part, we focus our attention on parasitic diseases caused by helminth and arthropod parasites shared between domestic and wild canids and felids in Europe. While a complete understanding of the biology, ecology and epidemiology of these parasites is particularly challenging to achieve, especially given the complexity of the environments in which these diseases perpetuate, advancements in current knowledge of transmission routes is crucial to provide policy-makers with clear indications on strategies to reduce the impact of these diseases on changing ecosystems.This is the author accepted manuscript. The final version is available from Elsevier at http://dx.doi.org/10.1016/j.vetpar.2015.04.02

A Comparative Study of Twin Delayed Deep Deterministic Policy Gradient and Soft Actor-Critic Algorithms for Robot Exploration and Navigation in Unseen Environments

This paper presents a comparison between twin- delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environ- ment complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios

Sterkfontein at 75: review of paleoenvironments, fauna, dating and archaeology from the hominin site of Sterkfontein (Gauteng Province, South Africa).

Seventy-five years after Robert Broom’s discovery of the first adult Australopithecus in 1936, the Sterkfontein Caves (Gauteng Province, South Africa) remains one of the richest and most informative fossil hominin sites in the world. The deposits record hominin and African mammal evolution from roughly 2.6 million years (Ma) until the Upper Pleistocene. Earlier excavation efforts focused on the Member 4 australopithecine-bearing breccia and the Member 5 stone tool-bearing breccias of Oldowan and Early Acheulean age. Ronald J. Clarke’s 1997 programme of understanding the cave deposits as a whole led to the discovery of the near-complete StW 573 Australopithecus skeleton in the Member 2 deposit of the Silberberg Grotto, and the exploration of lesser known deposits such as the Jacovec Cavern, Name Chamber and the Lincoln Cave. Our aim is to produce a cogent synthesis of the environments, palaeodietary information, fauna and stone artefacts as recorded in the Sterkfontein sequence. We begin with an overview of the site and early accounts of the interpretations of the site-formation processes, after which we discuss each Member in turn and summarize the various types of evidence published so far. Finally, we review the most pertinent debates about the site, including the ages of Sterkfontein Member 2 and 4, and the types of habitats represented at the site through time