Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning perspective (e.g., input space dimension, amount of data needed). The accuracy of such models, regardless of the learning techniques, relies on proper excitation and exploration of the robot's configuration space, in order to collect a rich dataset. This study aims to provide rich data in learning the inverse dynamics of a serial robotic manipulator using supervised machine learning techniques. We propose a method, called Max-Information Configuration Exploration (MICE), to incrementally explore and generate information-rich data via computing parameters of a trajectory set. We also introduce a new set of excitation trajectories that explores robot's configuration through imposed stable limit cycles in robot joints' phase space while satisfying feasibility constraints and physical bounds. We benchmark MICE against state-of-the-art in terms of data quality and learning accuracy. The proposed methodology for data collection, model learning, and evaluation, is validated with a KUKA IIWA14 robotic arm where the results prove significant improvement over traditional approaches.

From High-Level to Low-Level Robot Learning of Complex Tasks: Leveraging Priors, Metrics and Dynamical Systems

Nadia Barbara Figueroa Fernandez

Humans have a remarkable way of learning, adapting and mastering new manipulation tasks. With the current advances in Machine Learning (ML), the promise of having robots with such capabilities seems to be on the cusp of reality. Transferring human-level skills to robots, however, is complicated as they involve a level of complexity that cannot be tackled by classical ML methods in an unsupervised way. Such complexities involve: (i) automatically decomposing tasks into control-oriented encodings, (ii) extracting invariances and handling idiosyncrasies of data acquired from human demonstrations, and (iii) learning models that guarantee stability and convergence. In this thesis, we push the boundaries in the learning from demonstration (LfD) domain by addressing these challenges with minimal human intervention and parameter tuning. We introduce novel learning approaches based on Bayesian non-parametrics and kernel-methods while encoding novel metrics and priors, leveraging them with dynamical systems (DS) theory. In the first part of this thesis we focus on learning complex sequential manipulation tasks from heterogeneous and unstructured demonstrations. Such tasks are those composed of a sequence of discrete actions. The particular challenge is learning these tasks without any prior knowledge on the number of actions (unstructuredness) or restrictions as to how the human is demonstrating the task (heterogeneous), e.g. changes in reference frames. We propose a Bayesian non-parametric learning framework that can jointly segment and discover unique discrete actions (and their sequence) in continuous demonstrations of the task. Hence, we learn an entire task from a continuous, unrestricted natural demonstration. The learned structure of the complex tasks and the segmented data were then used to parametrize a hybrid controller to execute two cooking tasks of increasing complexity: (i) single-arm pizza dough rolling, and (ii) a dual-arm vegetable peeling task. Throughout this thesis, we assume that both the human and robot motions are driven by autonomous state-dependent DS. Hence, in the second part of this thesis we offer two novel DS formulations to represent and execute a complex task. We begin by proposing a DS-based motion generator formulation and learning scheme that is capable of automatically encoding continuous and complex motions while ensuring global asymptotic stability. The type of tasks that can be learned with this approach are unparalleled to previous work in DS-based LfD and are validated on production line and household activities. Further, we propose a novel DS formulation and learning scheme that can encode both complex motions and varying impedance requirements along the task; i.e., the robot must be compliant in some regions of the task, while stiff in others. This approach is validated on trajectory tracking tasks where a robot arm must precisely draw letters on a surface. Due to the generalization power and straight-forward learning schemes of the proposed DS-based motion generators, we also apply them to more complex application. Such applications include providing adaptive (i) navigation strategies for mobile agents, and (ii) locomotion and co-manipulation tasks of biped robots. These applications are particularly novel in the LfD domain, as most work is solely focused on robotic arm control. In the last part of the thesis, we explore learning complex behaviors in joint space for single and multi-arm systems.

EPFL2019

Adaptive sensorimotor peripersonal space representation and motor learning for a humanoid robot

Micha Hersch

This thesis presents possible computational mechanisms by which a humanoid robot can develop a coherent representation of the space within its reach (its peripersonal space), and use it to control its movements. Those mechanisms are inspired by current theories of peripersonal space representation and motor control in humans, targeting a cross-fertilization between robotics on one side, and cognitive science on the other side. This research addresses the issue of adaptivity the sensorimotor level, at the control level and at the level of simple task learning. First, this work considers the concept of body schema and suggests a computational translation of this concept, appropriate for controlling a humanoid robot. This model of the body schema is adaptive and evolves as a result of the robot sensory experience. It suggests new avenues for understanding various psychophysical and neuropsychological phenomenons of human peripersonal space representation such as adaptation to distorted vision and tool use, fake limbs experiments, body-part centered receptive fields, and multimodal neurons. Second, it is shown how the motor modality can be added to the body schema. The suggested controller is inspired by the dynamical system theory of motor control and allows the robot to simultaneously and robustly control its limbs in joint angles space and in end-effector location space. This amounts to controlling the robot in both proprioceptive and visual modalities. This multimodal control can benefit from the advantages offered by each modality and is better than traditional robotic controllers in several respects. It offers a simple and elegant solution to the singularity and joint limit avoidance problems and can be seen as a generalization of the Damped Least Square approach to robot control. The controller exhibits several properties of human reaching movements, such as quasi-straight hand paths and bell-shaped velocity profiles and non-equifinality. In a third step, the motor modalities is endowed with a statistical learning mechanism, based on Gaussian Mixture Models, that enables the humanoid to learn motor primitives from demonstrations. The robot is thus able to learn simple manipulation tasks and generalize them to various context, in a way that is robust to perturbations occurring during task execution. In addition to simulation results, the whole model has been implemented and validated on two humanoid robots, the Hoap3 and the iCub, enabling them to learn their arm and head geometries, perform reaching movements, adapt to unknown tools, and visual distortions, and learn simple manipulation tasks in a smooth, robust and adaptive way. Finally, this work hints at possible computational interpretations of the concepts of body schema, motor perception and motor primitives.

EPFL2009

From High-Level to Low-Level Robot Learning of Complex Tasks: Leveraging Priors, Metrics and Dynamical Systems

Nadia Barbara Figueroa Fernandez

EPFL2019

Adaptive sensorimotor peripersonal space representation and motor learning for a humanoid robot

Micha Hersch

EPFL2009