Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators

In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

Optimization of polymer processing: a review (Part II - Molding technologies)

The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Furthermore, within the field of artificial intelligence, several approaches can reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies to single and twin screw extruders, extrusion dies and calibrators. This second part focuses on injection molding, blow molding and thermoforming technologies.This research was funded by NAWA-Narodowa Agencja Wymiany Akademickiej, under grant PPN/ULM/2020/1/00125 and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No 734205–H2020-MSCA-RISE-2016. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/05256/2020, UIDP/05256/2020

Foundations of Trusted Autonomy

Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie

Incremental Neuroevolution of Reactive and Deliberative 3D Agents

Following earlier work on the neuroevolution of deliberative behaviour to solve increasingly challenging tasks in a twodimensional dynamic world, this paper presents the results of extending the original system to a three-dimensional rigid body simulation. The 3D physically based setting requires that a successful agent continually and deliberately adjust its gait, turning and other motor control over the many stages and sub-stages of these tasks, within its individual evaluation. Achieving such complex interplay between motor control and deliberative control, within a neuroevolutionary framework, is the focus of this work. To this end, a novel neural architecture is presented and an incremental evolutionary approach used to bootstrap the locomotive behaviour of the agents. Agent morphology is fixed as a quadruped with three degrees of freedom per limb. Agent populations have no initial knowledge of the problem domain, and evolve to move around and then solve progressively more difficult challenges in the environment using a tournament-based co-evolutionary algorithm. The results demonstrate not only success at the tasks but also a variety of intricate lifelike behaviours being used, separately and in combination, to achieve this success. Given the problem-agnostic controller architecture, these results indicate a potential for discovering yet more advanced behaviours in yet more complex environments

Affective neuroscience, emotional regulation, and international relations

International relations (IR) has witnessed an emerging interest in neuroscience, particularly for its relevance to a now widespread scholarship on emotions. Contributing to this scholarship, this article draws on the subfields of affective neuroscience and neuropsychology, which remain largely unexplored in IR. Firstly, the article draws on affective neuroscience in illuminating affect's defining role in consciousness and omnipresence in social behavior, challenging the continuing elision of emotions in mainstream approaches. Secondly, it applies theories of depth neuropsychology, which suggest a neural predisposition originating in the brain's higher cortical regions to attenuate emotional arousal and limit affective consciousness. This predisposition works to preserve individuals' self-coherence, countering implicit assumptions about rationality and motivation within IR theory. Thirdly, it outlines three key implications for IR theory. It argues that affective neuroscience and neuropsychology offer a route towards deep theorizing of ontologies and motivations. It also leads to a reassessment of the social regulation of emotions, particularly as observed in institutions, including the state. It also suggests a productive engagement with constructivist and poststructuralist approaches by addressing the agency of the body in social relations. The article concludes by sketching the potential for a therapeutically-attuned approach to IR

Tackling Credit Assignment Using Memory and Multilevel Optimization for Multiagent Reinforcement Learning

There is growing commercial interest in the use of multiagent systems in real world applications. Some examples include inventory management in warehouses, smart homes, planetary exploration, search and rescue, air-traffic management and autonomous transportation systems. However, multiagent coordination is an extremely challenging problem. First, information relevant for coordination is often distributed across the team members, and fragmented amongst each agent's observation histories (past states). Second, the coordination objective is often sparse and noisy from the perspective of an agent. Designing general mechanisms of generating agent-specific reward functions that incentivizes an agent to collaborate towards the shared global objective is extremely difficult. From a learning perspective, both difficulties can be linked to the difficulty of credit assignment - the process of accurately associating rewards with actions. The primary contribution of this dissertation is to tackle credit assignment in multiagent systems in order to enable better multiagent coordination. First we leverage memory as a tool in enabling better credit assignment by facilitating associations between rewards and actions separated across time. We achieve this by introducing Modular Memory Units (MMU), a memory-augmented neural architecture that can reliably retain and propagate information over an extended period of time. We then use MMU to augment individual agents' policies in solving dynamic tasks that require adaptive behavior from a distributed multiagent team. We also introduce Distributed MMU (DMMU) which uses memory as a shared knowledge base across a team of distributed agents to enable distributed one-shot decision making. Switching our attention from the agent to the learning algorithm, we then introduce Evolutionary Reinforcement Learning (ERL), a multilevel optimization framework that blends the strength of policy gradients and evolutionary algorithms to improve learning. We further extend the ERL framework to introduce Collaborative ERL (CERL) which employs a collection of policy gradient learners (portfolio), each optimizing over varying resolution of the same underlying task. This leads to a diverse set of policies that are able to reach diverse regions within the solution space. Results in a range of continuous control benchmarks demonstrate that ERL and CERL significantly outperform their composite learners while remaining overall more sample-efficient. Finally, we introduce Multiagent ERL (MERL), a hybrid algorithm that leverages the multilevel optimization framework of ERL to enable improved multiagent coordination without requiring explicit alignment between local and global reward functions. MERL uses fast, policy-gradient based learning for each agent by utilizing their dense local rewards. Concurrently, evolution is used to recruit agents into a team by directly optimizing the sparser global objective. Experiments in multiagent coordination benchmarks demonstrate that MERL's integrated approach significantly outperforms the state-of-the-art multiagent policy-gradient algorithms