Bimanual Robot Skills Mp Encoding Dimensionality Reduction And Reinforcement Learning
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Bimanual Robot Skills: MP Encoding, Dimensionality Reduction and Reinforcement Learning
In our culture, robots have been in novels and cinema for a long time, but it has been specially in the last two decades when the improvements in hardware - better computational power and components - and advances in Artificial Intelligence (AI), have allowed robots to start sharing spaces with humans. Such situations require, aside from ethical considerations, robots to be able to move with both compliance and precision, and learn at different levels, such as perception, planning, and motion, being the latter the focus of this work. The first issue addressed in this thesis is inverse kinematics for redundant robot manipulators, i.e: positioning the robot joints so as to reach a certain end-effector pose. We opt for iterative solutions based on the inversion of the kinematic Jacobian of a robot, and propose to filter and limit the gains in the spectral domain, while also unifying such approach with a continuous, multipriority scheme. Such inverse kinematics method is then used to derive manipulability in the whole workspace of an antropomorphic arm, and the coordination of two arms is subsequently optimized by finding their best relative positioning. Having solved the kinematic issues, a robot learning within a human environment needs to move compliantly, with limited amount of force, in order not to harm any humans or cause any damage, while being as precise as possible. Therefore, we developed two dynamic models for the same redundant arm we had analysed kinematically: The first based on local models with Gaussian projections, and the second characterizing the most problematic term of the dynamics, namely friction. Such models allowed us to implement feed-forward controllers, where we can actively change the weights in the compliance-precision tradeoff. Moreover, we used such models to predict external forces acting on the robot, without the use of force sensors. Afterwards, we noticed that bimanual robots must coordinate their components (or limbs) and be able to adapt to new situations with ease. Over the last decade, a number of successful applications for learning robot motion tasks have been published. However, due to the complexity of a complete system including all the required elements, most of these applications involve only simple robots with a large number of high-end technology sensors, or consist of very simple and controlled tasks. Using our previous framework for kinematics and control, we relied on two types of movement primitives to encapsulate robot motion. Such movement primitives are very suitable for using reinforcement learning. In particular, we used direct policy search, which uses the motion parametrization as the policy itself. In order to improve the learning speed in real robot applications, we generalized a policy search algorithm to give some importance to samples yielding a bad result, and we paid special attention to the dimensionality of the motion parametrization. We reduced such dimensionality with linear methods, using the rewards obtained through motion repetition and execution. We tested such framework in a bimanual task performed by two antropomorphic arms, such as the folding of garments, showing how a reduced dimensionality can provide qualitative information about robot couplings and help to speed up the learning of tasks when robot motion executions are costly.
Reinforcement Learning of Bimanual Robot Skills
This book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process. It proposes practical enhancements to closed-loop inverse kinematics for redundant robots, a procedure to position the two arms to maximize workspace manipulability, and a dynamic model together with a disturbance observer to achieve compliant control and safe robot behavior. In the second part, methods for robot motion learning based on movement primitives and direct policy search algorithms are presented. To improve sampling efficiency and accelerate learning without deteriorating solution quality, techniques for dimensionality reduction, for exploiting low-performing samples, and for contextualization and adaptability to changing situations are proposed. In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot motion learning.
Using Symmetries in Reinforcement Learning of Bimanual Robotic Tasks
The learning of bimanual robotic tasks, i.e., tasks executed by two manipulators together, can be particularly important in the new scenarios opened by the rise of humanoid robotics, one of the most interesting trend currently in the field. The work presented wants to build a method to simplify the dimensionality of parameter space in this particular context, exploiting the presence of symmetries between the movements executed by the two arms. The aim is to develop a reduced-order representation of the bimanual motion, with the purpose of increase the speed of learning process. In chapter 1, kinematics of the used robots is studied, in order to know how to correctly command the position of the robots while executing a task. Robotic movements are then modeled using Probabilistic Movement Primitives (ProMPs), a stochastic interpretation of robot movements (details in chapter 2). The first objective is to develop a symmetrization method for those kind of policies, and this part is treated in chapter 3. This will give the chance of representing the movement of two robotic arms, with only a single ProMP (instead of two, one for each arm), from which obtain the second policy applying symmetrization. In this way the amount of parameters representing motion can be halved. The most common kind of symmetry is the one defined by a plane, but also other cases can be explored, e.g., spherical or cylindrical symmetry. If the symmetry surface is not explicitly given in the bimanual task description, it is critical to have a reliable method to estimate it in order to exploit it in the learning process. In chapter 4 it is reported a way to obtain this estimation of the parameters describing the symmetry surface from the initially demonstrated trajectories. Finally, in chapter 5 it is defined a symmetric policy representation for bimanual task, that depends only on a single ProMP and a symmetry surface. The effectiveness of this parameter reduction has been tested applying it in reinforcement learning of some tasks, in comparison to the results obtained by the standard way of proceeding, that model the bimanual task with two separated ProMPs, one for each robotic arm.