<p> </p> <p>The usage of robots has increased in different areas of society and human work, including medicine, transportation, education, space exploration, and the service industry. This phenomenon has generated a sudden enthusiasm to develop more intelligent robots that are better equipped to perform tasks in a manner that is equivalently good as those completed by humans. Such jobs require human involvement as operators or teammates since robots struggle with automation in everyday settings. Soon, the role of humans will be far beyond users or stakeholders and include those responsible for training such robots. A popular teaching form is to allow robots to mimic human behavior. This method is intuitive and natural and does not require specialized knowledge of robotics. While there are other methods for robots to complete tasks effectively, collaborative tasks require mutual understanding and coordination that is best achieved by mimicking human motion. This mimicking problem has been tackled through skill imitation, which reproduces human-like motion during a task shown by a trainer. Skill imitation builds on faithfully replicating the human pose and requires two steps. In the first step, an expert's demonstration is captured and pre-processed, and motion features are obtained; in the second step, a learning algorithm is used to optimize for the task. The learning algorithms are often paired with traditional control systems to transfer the demonstration to the robot successfully. However, this methodology currently faces a generalization issue as most solutions are formulated for specific robots or tasks. The lack of generalization presents a problem, especially as the frequency at which robots are replaced and improved in collaborative environments is much higher than in traditional manufacturing. Like humans, we expect robots to have more than one skill and the same skills to be completed by more than one type of robot. Thus, we address this issue by proposing a human motion imitation framework that can be efficiently computed and generalized for different kinematic structures (e.g., different robots).</p> <p> </p> <p>This framework is developed by training an algorithm to augment collaborative demonstrations, facilitating the generalization to unseen scenarios. Later, we create a model for pose imitation that converts human motion to a flexible constraint space. This space can be directly mapped to different kinematic structures by specifying a correspondence between the main human joints (i.e., shoulder, elbow, wrist) and robot joints. This model permits having an unlimited number of robotic links between two assigned human joints, allowing different robots to mimic the demonstrated task and human pose. Finally, we incorporate the constraint model into a reward that informs a Reinforcement Learning algorithm during optimization. We tested the proposed methodology in different collaborative scenarios. Thereafter, we assessed the task success rate, pose imitation accuracy, the occlusion that the robot produces in the environment, the number of collisions, and finally, the learning efficiency of the algorithm.</p> <p> </p> <p>The results show that the proposed framework creates effective collaboration in different robots and tasks.</p>

Human-Robot Collaboration (HRC), where both humans and robots work in the same environment simultaneously, is an emerging field and has increased massively during the past decade. For this collaboration to be feasible and safe, robots need to perform a proper safety analysis to avoid hazardous situations. This safety analysis procedure involves complex computer vision tasks that require a lot of processing power. Therefore, robots with constrained computing resources cannot execute these tasks without any delays, thus for executing these tasks they rely on edge infrastructures, such as remote computational resources accessible over wireless communication. In some cases though, the edge may be unavailable, or connection to it may not be possible. In such cases, robots still have to navigate themselves around the environment, while maintaining high levels of safety. This thesis project focuses on reducing the complexity and the total number of parameters of pre-trained computer vision models by using model compression techniques, such as pruning and knowledge distillation. These model compression techniques have strong theoretical and practical foundations, but work on their combination is limited, therefore it is investigated in this work. The results of this thesis project show that in the test cases, up to 90% of the total number of parameters of a computer vision model can be removed without any considerable reduction in the model’s accuracy. / Människa och robot samarbete (förkortat HRC från engelskans Human-Robot Collaboration), där både människor och robotar arbetar samtidigt i samma miljö, är ett växande forskningsområde och har ökat dramatiskt över de senaste decenniet. För att detta samarbetet ska vara möjligt och säkert behöver robotarna genomgå en ordentlig säkerhetsanalys så att farliga situationer kan undvikas. Denna säkerhetsanalys inkluderar komplexa Computer Vision uppgifter som kräver mycket processorkraft. Därför kan inte robotar med begränsad processorkraft utföra dessa beräkningar utan fördröjning, utan måste istället förlita sig på utomstående infrastruktur för att exekvera dem. Vid vissa tillfällen kan dock denna utomstående infrastruktur inte finnas på plats eller vara svår att koppla upp sig till. Även vid dessa tillfällen måste robotar fortfarande kunna navigera sig själva genom en lokal, och samtidigt upprätthålla hög grad av säkerhet. Detta projekt fokuserar på att reducera komplexiteten och det totala antalet parametrar av för-tränade Computer Vision-modeller genom att använda modellkompressionstekniker så som: Beskärning och kunskapsdestilering. Dessa modellkompressionstekniker har starka teoretiska grunder och praktiska belägg, men mängden arbeten kring deras kombinerade effekt är begränsad, därför är just det undersökt i detta arbetet. Resultaten av det här projektet visar att up till 90% av det totala antalet parametrar hos en Computer Vision-modell kan tas bort utan någon noterbar försämring av modellens säkerhet.