Learning Motion Skills for a Humanoid Robot

This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters. Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed. Diese Dissertation beschäftigt sich mit dem Lernen von Bewegungsfähigkeiten für humanoide Roboter. Als Grundlage wurde zunächst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene Ansätze für die Erstellung von Bewegungsfähigkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt. Danach wurde ein Ansatz basierend auf bestärkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide Ansätze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die Fähigkeit des Gehens gelegt, aber auch Aufsteh- und Schussfähigkeiten werden diskutiert

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Towards Safe Autonomy in Assistive Robots

Robots have the potential to support older adults and persons with disabilities on a direct and personal level. For example, a wearable robot may help a person stand up from a chair, or a robotic manipulator may aid a person with meal preparation and housework. Assistive robots can autonomously make decisions about how best to support a person. However, this autonomy is potentially dangerous; robots can cause collisions or falls which may lead to serious injury. Therefore, guaranteeing that assistive robots operate safely is imperative. This dissertation advances safe autonomy in assistive robots by developing a suite of tools for the tasks of perception, monitoring, manipulation and all prevention. Each tool provides a theoretical guarantee of its correct performance, adding a necessary layer of trust and protection when deploying assistive robots. The topic of interaction, or how a human responds to the decisions made by assistive robots, is left for future work. Perception: Assistive robots must accurately perceive the 3D position of a person's body to avoid collisions and build predictive models of how a person moves. This dissertation formulates the problem of 3D pose estimation from multi-view 2D pose estimates as a sum-of-squares optimization problem. Sparsity is leveraged to efficiently solve the problem, which includes explicit constraints on the link lengths connecting any two joints. The method certifies the global optimality of its solutions over 99 percent of the time, and matches or exceeds state-of-the-art accuracy while requiring less computation time and no 3D training data. Monitoring: Assistive robots may mitigate fall risk by monitoring changes to a person’s stability over time and predicting instabilities in real time. This dissertation presents Stability Basins which characterize stability during human motion, with a focus on sit-to-stand. An 11-person experiment was conducted in which subjects were pulled by motor-driven cables as they stood from a chair. Stability Basins correctly predicted instability (stepping or sitting) versus task success with over 90 percent accuracy across three distinct sit-to-stand strategies. Manipulation: Robotic manipulators can support many common activities like feeding, dressing, and cleaning. This dissertation details ARMTD (Autonomous Reachability-based Manipulator Trajectory Design) for receding-horizon planning of collision-free manipulator trajectories. ARMTD composes reachable sets of the manipulator through workspace from low dimensional trajectories of each joint. ARMTD creates strict collision-avoidance constraints from these sets, which are enforced within an online trajectory optimization. The method is demonstrated for real-time planning in simulation and on hardware on a Fetch Mobile Manipulator robot, where it never causes a collision. Fall Prevention: Wearable robots may prevent falls by quickly reacting when a user trips or slips. This dissertation presents TRIP-RTD (Trip Recovery in Prostheses via Reachability-based Trajectory Design), which extends the ARMTD framework to robotic prosthetic legs. TRIP-RTD uses predictions of a person’s response to a trip to plan recovery trajectories of a prosthetic leg. TRIP-RTD creates constraints for an online trajectory optimization which ensure the prosthetic foot is placed correctly across a range of plausible human responses. The approach is demonstrated in simulation using data of non-amputee subjects being tripped.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169822/1/pdholmes_1.pd