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Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods attempt to address this issue, they often struggle to learn human-like gaits because of the complexity involved in engineering an effective reward function. In this paper, we demonstrate that adversarial imitation learning c

Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning. However, when dynamic motions under constraints are needed and computation time is limited, fast kinodynamic planning on the constraint manifold is indispensable. In recent years, learning-based solutions have

Genomgång av samtliga de tillfällen då Carl von Linné omnämns i Carl Tersmedens memoarer inklusive kommentarer, bland annat rörande kronologiska diskrepanser.

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real rob

Recent methods for imitation learning directly learn a Q-function using an implicit reward formulation rather than an explicit reward function.However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states.Previous works show that a squared norm regularization on the implicit reward function is effective, but do not provide a theore

Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras. This paper presents a first step towards a ro

Black-box policy optimization is a class of reinforcement learning algorithms that explores and updates the policies at the parameter level. This class of algorithms is widely applied in robotics with movement primitives or non-differentiable policies. Furthermore, these approaches are particularly relevant where exploration at the action level could cause actuator damage or other safety issues. H

Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting

Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector f

Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More

Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achieved by 'black-box' policies in simulated domains. When deploying RL to the real world, several concerns regarding the use of a 'black-box' policy might be raised. In order to make the learned policies more transparent, we

The report is a risk analysis of the hydrogen production plant at Ringhals Nuclear Power Plant, Sweden. The report also discusses potential hazards from the handling and storage of hydrogen gas. Principles of hydrogen gas detection and sensor technology are presented. A HAZOP-analysis of the plant is carried out, and recommendations of improvements based on the results of the analysis are made. Co

An inventory and analysis of extinguishing agents and systems. A quantitative analysis of the weight of the systems and of the damage on hull material for different fire scenarios. Tests have been carried out in cone calorimeter and simulations in HSLAB and FREIA:

A probabilistic riskanalysis of a railway tunnel in Örnsköldsvik, Sweden. Using societal risk showing the hazard of the system. The analysis indicates a high risk greater than recommended levels of acceptance according to international risk criteria. Furthermore proposal for hazard prevention in tunnels are made.

The purpose of this study is to evaluate the safety of people located in discotheques in case of fire. A quantitative fire riskanalysis (QRA) for three different types of discotheques is carried out. The analysis is based on performance based codes from which fire protection alternatives are analysed. The results of the quantitative fire riskanalysis are described as risk profiles and average risk

A serie of ten full scale fire scenerios has been analysed in a compartment with and without residentiual sprinkler. The production of carbon monoxide, carbon dioxide and oxygen was measered in two different heights in the compartment during the tests. (Swedish)