Whole-Body MPC for a Dynamically Stable Mobile Manipulator

Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening

Robotic Mobile Manipulation via Adaptive and Learning-Based Model Predictive Control

The use of dynamically stable mobile manipulators is expanding beyond controlled research laboratories and into the real world. However, autonomous manipulation skills are still specialized to individual tasks and can handle limited variations in the objects’ physical properties, which prevents robot deployment in unstructured human environments. This thesis focuses on whole-body motion planning and control for dynamically stable mobile manipulators and on providing controllers with real-time adaptation to changes in the robot dynamics due to the interaction with objects. Dynamically stable mobile manipulators, i.e., actively-balancing mobile robots equipped with robotic arms, are potentially very useful for working in environments designated for humans. However, their agility and compliance come at the cost of a high control complexity. Traditional control strategies treat the locomotion and manipulation problems separately, requiring additional heuristics to achieve whole-body coordination. Moreover, inverse-dynamics-based controllers do not consider the system’s future evolution, which is essential for balance control. On the other hand, in this thesis we propose a whole-body motion planning and control formulation based on Model Predictive Control (MPC). Our method leverages the full robot dynamics and jointly optimizes for balancing, base tracking, end-effector tracking, and environment interaction. We validate the proposed whole-body MPC controller in extensive experiments with a ball-balancing manipulator. When the robot dynamics is not known precisely or when manipulating new objects, model uncertainties can severely undermine the performance and generality of MPC. To tackle this problem, we propose two online adaptation schemes for the objects’ parameters used in the system dynamics of MPC, which we showcase in a door-opening and an object-lifting task with a ball-balancing manipulator. Although we initially model the external environment as a linear system, a more descriptive representation is necessary for more complex manipulation tasks or in the presence of uncertainties in the robot dynamics. Thus, we propose to approximate the model error as a linear combination of trigonometric basis functions. Assuming that the underlying structure of the dynamics does not vary significantly when the robot performs similar manipulation tasks, we learn the hyperparameters of the basis functions from data collected during related experiments, e.g., by letting the robot open doors with different stiffness coefficients. When executing a new task, the hyperparameters of the basis functions are kept fixed while the linear parameters are adapted online. We test the resulting multi-task learning MPC controller in simulations and hardware experiments, and present extensive comparisons against other adaptive MPC controllers. Finally, to obtain better tracking performance despite parametric uncertain- ties, we incorporate the Control Lyapunov Function (CLF) constraint derived in adaptive control for robot manipulators to the set of inequalities of the optimal control problem. Thus, we obtain an adaptive controller that combines the advantages of CLFs and MPC, yielding an improved performance during the robot’s interaction with unknown objects and a reduced dependence on the tuning of the MPC prediction horizon. We demonstrate the advantages of the proposed method with respect to several baselines, and we validate it in hardware tests on a quadrupedal robot carrying bricks and pulling heavy boxes

Whole-body balance control of multiplatform humanoid robots

The aims of this thesis are the study and experimental validation of laws for the dynamic balance control of multiplatform humanoid robots. With the expression multiplatform humanoid robot we refer to robots composed of a humanoid upper body and a mobile base. Indeed, typical inspection and mantainance or disaster scenarios require the employment of different kinds of mobile robots. Thus, we need to be able to fully exploit the dynamic capabilities of legged, wheeled or aerial mobile bases and to interact with the environment through a humanoid upper body to perform complex bimanipulation tasks, possibly in a teleoperated setting. First, we propose a new balancing control law for a legged humanoid robot, which has been validated through simulations. Secondly, the possibility of dynamically controlling a wheeled humanoid robot is analyzed. The balancing problem is solved through an inverse dynamics approach, and experimental results are presented to show the effectiveness of the proposed method. Moreover, we propose a momentum-based control strategy as an extension for a humanoid robot with a quadrotor flying mobile base

Deep Measurement Updates for Bayes Filters

Measurement update rules for Bayes filters often contain hand-crafted heuristics to compute observation probabilities for high-dimensional sensor data, like images. In this work, we propose the novel approach Deep Measurement Update (DMU) as a general update rule for a wide range of systems. DMU has a conditional encoder-decoder neural network structure to process depth images as raw inputs. Even though the network is trained only on synthetic data, the model shows good performance at evaluation time on real-world data. With our proposed training scheme primed data training , we demonstrate how the DMU models can be trained efficiently to be sensitive to condition variables without having to rely on a stochastic information bottleneck. We validate the proposed methods in multiple scenarios of increasing complexity, beginning with the pose estimation of a single object to the joint estimation of the pose and the internal state of an articulated system. Moreover, we provide a benchmark against Articulated Signed Distance Functions(A-SDF) on the RBO dataset as a baseline comparison for articulation state estimation.ISSN:2377-376

Expanding the phenotype of Brunner syndrome from childhood to adulthood: Description of the second pediatric patient and his mother

Brunner syndrome is a recessive X-linked disorder caused by pathogenic variants in the monoamine oxidase A gene (MAOA). It is characterized by distinctive aggressive behavior, mild intellectual disability, sleep disturbances, and typical biochemical alterations deriving from the impaired monoamine metabolism. We herein describe a 5-year-old boy with developmental delay, autistic features, and myoclonic epilepsy, and his mother, who had mild intellectual disability and recurrent episodes of palpitations, headache, abdominal pain, and abdominal bloating. Whole exome sequencing allowed detection of the maternally-inherited variant c.410A>G, (p.Glu137Gly) in the MAOA gene. The subsequent biochemical studies confirmed the MAOA deficiency both in the child and his mother. Given the serotonergic symptoms associated with high serotonin levels found in the mother, treatment with a serotonin reuptake inhibitor and dietary modifications were carried out, resulting in regression of the biochemical abnormalities and partial reduction of symptoms. Our report expands the phenotypic spectrum of Brunner disease, bringing new perspectives on the behavioral and neurodevelopmental phenotype from childhood to adulthood