Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.Comment: accepted in Neural Network

Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network

It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences. Although many existing robotics studies use a forward model framework, there are generalization issues with high degrees of freedom. The current study shows that the predictive coding (PC) and active inference (AIF) frameworks, which employ a generative model, can develop better generalization by learning a prior distribution in a low dimensional latent state space representing probabilistic structures extracted from well habituated sensory-motor trajectories. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound, while goal-directed planning is accomplished by inferring latent variables for maximizing the estimated lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data by setting an intermediate value for a regularization coefficient. Furthermore, comparative simulation results show that the proposed model outperforms a conventional forward model in goal-directed planning, due to the learned prior confining the search of motor plans within the range of habituated trajectories.Comment: 30 pages, 19 figure

Learning and adaptation in physical agents

Learning and adaptation is fundamental for autonomous agents that operate in a physical world and not a computer network. The paper is providing a general framework of skills learning within behaviour logic framework of agents that communicate, sense and act in the physical world. It is advocated that playfulness can be important in learning and to improving skills of agents

Motivations for local climate adaptation in Dutch municipalities: climate change impacts and the role of local-level government

The local government level is considered to be crucial in preparing society for climate change impact. Yet little is known about why local authorities do or do not take action to adapt their community for climate change impacts. In order to implement effective adaptation policy, the motivations for local climate adaptation need to be examined. This paper explores these motivations in Dutch communities by comparing nine urban and rural cases. To be able to draw general conclusions, cases are selected on „projected risk‟ and „extreme weather event experience‟. Motivations for local climate adaptation appear much more determined by local institutional factors such as a green party aldermen or innovative network membership then projected risk or extreme weather event experience. This could be explained by the empiric data showing diffuse channels of climate change knowledge into the local government level and limited capacity to translate this knowledge into genuine adaptation strategie

Climate Action In Megacities 3.0

"Climate Action in Megacities 3.0" (CAM 3.0) presents major new insights into the current status, latest trends and future potential for climate action at the city level. Documenting the volume of action being taken by cities, CAM 3.0 marks a new chapter in the C40-Arup research partnership, supported by the City Leadership Initiative at University College London. It provides compelling evidence about cities' commitment to tackling climate change and their critical role in the fight to achieve global emissions reductions