Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users’ preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users’ gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation

Modeling Emotional Aspects in Human Locomotion

The study of emotional body language has been the effort of many scientists for more than 200 years, from areas such as psychology, neuroscience, biology, and others. A lot of work has focused on the analysis of the kinematics, while the study of the underlying dynamics is still largely unexplored. In this thesis we model human walking as a nonlinear multi-phase optimal control problem to investigate the dynamics of full-body emotional expressions in human locomotion. Our approach is based on rigid multibody dynamics, a highly parameterized mathematical model of the human locomotion system, and the direct multiple-shooting method to analyze the dynamics of recorded kinematic motion capture data. Modeling the dynamics of a human rigid multibody model results in a set of highly complex differential algebraic equations that require automated methods to derive and evaluate. We created a new rigid multibody dynamics software package to model and numerically evaluate kinematic and dynamic quantities of rigid multibody systems expressed in generalized coordinates, including modeling of external contacts and discontinuities arising from contact events. Our package evaluates components of the equation of motion for multibody systems using recursive algorithms that are based on Featherstone's 6-D spatial algebra notation. Our package is specifically tailored for the use in numerical optimal control and carefully designed to exploit sparsities and reduction of redundant computations by selectively reusing computed values. By doing so we are able to achieve and partially exceed performance that is otherwise only available with source code generation modeling approaches. We created a highly parameterized 3-D meta model for the human locomotion system. This rigid multibody model is based on biomechanical data for kinematic and inertial parameters and enables us to create subject-specific dynamic models by adjusting segment dimensions, joint locations, and inertial parameters. To describe the contact between the human model and the ground, we created a non-holonomic rigid body contact model specifically for human walking movements that approximates the foot geometry using a sphere for the heel and a line segment at the ball of the foot during forefoot contact. Transforming motion capture marker data to rigid multibody motion is a difficult problem due to unknown joint centers, redundant marker movements, and non-rigid movement of markers as a result of skin and tissue movement. In this thesis, we developed and implemented a semi-automatic method in which we manually adjust the model to approximate the recorded subject and then compute joint angles by solving a non-linear least-squares optimization problem. Our approach is independent of the used motion capture marker set and directly maps onto the joint space of the model. We formulate two types of multi-phase optimal control problems for human walking: an inverse reconstruction problem and a gait synthesis problem that both have the differential equations of the rigid multibody dynamics as a constraint and can be used for different purposes. The reconstruction problem computes the unknown joint actuations from purely kinematic motion capture data. Applied to the recorded motion capture data, the reconstructed joint actuations show emotion specific features that are also found in the recorded muscle activity. This validates our model and approach to use optimal control problems as a tool to study emotional body language in a new way. Our gait synthesis formulation allows the generation of walking motions solely based on mathematical and physical principles. It can be applied in computer animation, robotics, and predictive gait analysis. We have generated a wide range of motions by adjusting objective function and gait parameters. A long-term goal of this formulation is to investigate optimality criteria of emotional walking motions. For this, we aim to use hierarchical optimal control problems in our future works

Frequency-Aware Model Predictive Control

Transferring solutions found by trajectory optimization to robotic hardware remains a challenging task. When the optimization fully exploits the provided model to perform dynamic tasks, the presence of unmodeled dynamics renders the motion infeasible on the real system. Model errors can be a result of model simplifications, but also naturally arise when deploying the robot in unstructured and nondeterministic environments. Predominantly, compliant contacts and actuator dynamics lead to bandwidth limitations. While classical control methods provide tools to synthesize controllers that are robust to a class of model errors, such a notion is missing in modern trajectory optimization, which is solved in the time domain. We propose frequency-shaped cost functions to achieve robust solutions in the context of optimal control for legged robots. Through simulation and hardware experiments we show that motion plans can be made compatible with bandwidth limits set by actuators and contact dynamics. The smoothness of the model predictive solutions can be continuously tuned without compromising the feasibility of the problem. Experiments with the quadrupedal robot ANYmal, which is driven by highly-compliant series elastic actuators, showed significantly improved tracking performance of the planned motion, torque, and force trajectories and enabled the machine to walk robustly on terrain with unmodeled compliance

New control strategies for neuroprosthetic systems

The availability of techniques to artificially excite paralyzed muscles opens enormous potential for restoring both upper and lower extremity movements with\ud neuroprostheses. Neuroprostheses must stimulate muscle, and control and regulate the artificial movements produced. Control methods to accomplish these tasks include feedforward (open-loop), feedback, and adaptive control. Feedforward control requires a great deal of information about the biomechanical behavior of the limb. For the upper extremity, an artificial motor program was developed to provide such movement program input to a neuroprosthesis. In lower extremity control, one group achieved their best results by attempting to meet naturally perceived gait objectives rather than to follow an exact joint angle trajectory. Adaptive feedforward control, as implemented in the cycleto-cycle controller, gave good compensation for the gradual decrease in performance observed with open-loop control. A neural network controller was able to control its system to customize stimulation parameters in order to generate a desired output trajectory in a given individual and to maintain tracking performance in the presence of muscle fatigue. The authors believe that practical FNS control systems must\ud exhibit many of these features of neurophysiological systems

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Analyzing Whole-Body Pose Transitions in Multi-Contact Motions

When executing whole-body motions, humans are able to use a large variety of support poses which not only utilize the feet, but also hands, knees and elbows to enhance stability. While there are many works analyzing the transitions involved in walking, very few works analyze human motion where more complex supports occur. In this work, we analyze complex support pose transitions in human motion involving locomotion and manipulation tasks (loco-manipulation). We have applied a method for the detection of human support contacts from motion capture data to a large-scale dataset of loco-manipulation motions involving multi-contact supports, providing a semantic representation of them. Our results provide a statistical analysis of the used support poses, their transitions and the time spent in each of them. In addition, our data partially validates our taxonomy of whole-body support poses presented in our previous work. We believe that this work extends our understanding of human motion for humanoids, with a long-term objective of developing methods for autonomous multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots (Humanoids) 201

Dynamics simulation of human box delivering task

Thesis (M.S.) University of Alaska Fairbanks, 2018The dynamic optimization of a box delivery motion is a complex task. The key component is to achieve an optimized motion associated with the box weight, delivering speed, and location. This thesis addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, transition step, carrying, transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying task, the objective function is the dynamic effort. The unloading task is a byproduct of the lifting task, but done in reverse, starting with holding the box and ending with it at its final position. In contrast, for transition task, the objective function is the combination of dynamic effort and joint discomfort. The various joint parameters are analyzed consisting of joint torque, joint angles, and ground reactive forces. A viable optimization motion is generated from the simulation results. It is also empirically validated. This research holds significance for professions containing heavy box lifting and delivering tasks and would like to reduce the chance of injury.Chapter 1 Introduction -- Chapter 2 Skeletal Human Modeling -- Chapter 3 Kinematics and Dynamics -- Chapter 4 Lifting Simulation -- Chapter 5 Carrying Simulation -- Chapter 6 Delivering Simulation -- Chapter 7 Conclusion and Future Research -- Reference