Martin L. Felis

Bio: Martin L. Felis is an academic researcher from Interdisciplinary Center for Scientific Computing. The author has contributed to research in topics: Optimal control & Humanoid robot. The author has an hindex of 8, co-authored 14 publications receiving 297 citations. Previous affiliations of Martin L. Felis include Heidelberg University.

RBDL: an efficient rigid-body dynamics library using recursive algorithms

Abstract: In our research we use rigid-body dynamics and optimal control methods to generate 3-D whole-body walking motions. For the dynamics modeling and computation we created RBDL--the Rigid Body Dynamics Library. It is a self-contained free open-source software package that implements state of the art dynamics algorithms including external contacts and collision impacts. It is based on Featherstone's spatial algebra notation and is implemented in C++ using highly efficient data structures that exploit sparsities in the spatial operators. The library contains various helper methods to compute quantities, such as point velocities, accelerations, Jacobians, angular and linear momentum and others. A concise programming interface and minimal dependencies makes it suitable for integration into existing frameworks. We demonstrate its performance by comparing it with state of the art dynamics libraries both based on recursive evaluations and symbolic code generation.

Synthesis of full-body 3-D human gait using optimal control methods

Abstract: In this paper we present a method that uses optimal control for offline human gait synthesis that does not depend on motion capture data or task-specific controllers. Our method is based on efficient simulation of rigid multibody systems and a direct multiple-shooting method to solve the underlying space-time optimization problem. We formulated different optimization criteria and synthesized gaits for a fullbody 3-D human model with 34 degrees of freedom and compared the resulting movements with human data. By combining different criteria we are able to improve the similarity of the synthesized motions with respect to recorded human motioncapture motions.

An optimal control approach to reconstruct human gait dynamics from kinematic data

Abstract: A common approach to record full-body human movement data is by using marker based motion capture systems. To obtain dynamic gait data such as joint torques and ground reaction forces additional measurement devices have to be employed that pose restrictions on where feet have to be placed during the recording. In this paper we use articulated rigid multibody models and optimal control methods to recover dynamic gait data solely from kinematic data. Our approach is independent from the used marker set and creates the rigid multibody model and computes all controls for the model such that when applied to the model, it closely reproduces the originally recorded motion. To achieve this there are two steps involved: i) create a subject-specific rigid multibody model of the recorded person and used marker set and compute the joint kinematics using inverse kinematics. ii) reconstruct the gait dynamics by solving an optimal control problem. For step i) we created a parameterize human model HEIMAN and a graphical user interface PUPPETEER that facilitates creation of the subject specific model and the motion capture mapping. For ii) we use MUSCOD-II, which implements the direct multiple-shooting method. We apply our method on 15 emotional human walking motions to compare joint angle and torque patterns of different emotions.

Inverse optimal control based identification of optimality criteria in whole-body human walking on level ground

Abstract: Understanding the underlying concepts of human locomotion is important for many fields of research. Based on the assumption that human motions are optimal, we propose an inverse optimal control (IOC) based approach to identify the optimality criteria in human walking. To this end, human walking is modeled as a non-linear optimal control problem with a linear combination of elementary optimality functions as objective and a hybrid dynamics multi-body system as constraints. The developed IOC-framework is set up in a modular way and exploits the natural bi-level structure of the problem. It allows for a great flexibility in the choice of outer optimization techniques and inner dynamic models. In the present work, we use the developed IOC approach to identify weights of seven elementary criteria for seven walking motions captured from six different subjects. The considered optimality criteria address the minimization of joint torques for four sets of joints, head stabilization, the step length, and the step frequency. For all trials the algorithm performs successfully. Even though the identified weights differ observably between subjects, which explains the different walking styles, the correlation matrix gives rise to the hypothesis that there exists a significant correlation of optimality across subjects. The identification of optimality criteria in human walking is a very important issue for all disciplines, where a prediction of human behavior is needed. For example in medical applications to improve therapies or to develop new mobility devices, in sport science to improve training plans or in humanoid robotics to develop new walking strategies.

Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model

Abstract: Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution towards bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation.

The Pinocchio C++ library : A fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives

Abstract: We introduce Pinocchio, an open-source software framework that implements rigid body dynamics algorithms and their analytical derivatives. Pinocchio does not only include standard algorithms employed in robotics (e.g., forward and inverse dynamics) but provides additional features essential for the control, the planning and the simulation of robots. In this paper, we describe these features and detail the programming patterns and design which make Pinocchio efficient. We evaluate the performances against RBDL, another framework with broad dissemination inside the robotics community. We also demonstrate how the source code generation embedded in Pinocchio outperforms other approaches of state of the art.

Learning symmetric and low-energy locomotion

Abstract: Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement learning (DRL) is a promising approach for the automatic creation of locomotion control. Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function [Duan et al. 2016]. Although several different DRL algorithms can successfully create a running controller, the resulting motions usually look nothing like a real runner. This paper takes a minimalist learning approach to the locomotion problem, without the use of motion examples, finite state machines, or morphology-specific knowledge. We introduce two modifications to the DRL approach that, when used together, produce locomotion behaviors that are symmetric, low-energy, and much closer to that of a real person. First, we introduce a new term to the loss function (not the reward function) that encourages symmetric actions. Second, we introduce a new curriculum learning method that provides modulated physical assistance to help the character with left/right balance and forward movement. The algorithm automatically computes appropriate assistance to the character and gradually relaxes this assistance, so that eventually the character learns to move entirely without help. Because our method does not make use of motion capture data, it can be applied to a variety of character morphologies. We demonstrate locomotion controllers for the lower half of a biped, a full humanoid, a quadruped, and a hexapod. Our results show that learned policies are able to produce symmetric, low-energy gaits. In addition, speed-appropriate gait patterns emerge without any guidance from motion examples or contact planning.

Analytical Derivatives of Rigid Body Dynamics Algorithms

Abstract: Rigid body dynamics is a well-established framework in robotics. It can be used to expose the analytic form of kinematic and dynamic functions of the robot model. So far, two major algorithms, namely the recursive Newton-Euler algorithm (RNEA) and the articulated body algorithm (ABA), have been proposed to compute the inverse dynamics and the forward dynamics in a few microseconds. Evaluating their derivatives is an important challenge for various robotic applications (optimal control, estimation, co-design or reinforcement learning). However it remains time consuming, whether using finite differences or automatic differentiation. In this paper, we propose new algorithms to efficiently compute them thanks to closed-form formulations. Using the chain rule and adequate algebraic differentiation of spatial algebra, we firstly differentiate explicitly RNEA. Then, using properties about the derivative of function composition, we show that the same algorithm can also be used to compute the derivatives of ABA with a marginal additional cost. For this purpose, we introduce a new algorithm to compute the inverse of the joint-space inertia matrix, without explicitly computing the matrix itself. All the algorithms are implemented in our open-source C++ framework called Pinocchio. Benchmarks show computational costs varying between 3 microseconds (for a 7-dof arm) up to 17 microseconds (for a 36-dof humanoid), outperforming the alternative approaches of the state of the art.

CENTAURO: A Hybrid Locomotion and High Power Resilient Manipulation Platform

Abstract: Despite the development of a large number of mobile manipulation robots, very few platforms can demonstrate the required strength and mechanical sturdiness to accommodate the needs of real-world applications with high payload and moderate/harsh physical interaction demands, e.g., in disaster-response scenarios or heavy logistics/collaborative tasks. In this letter, we introduce the design of a wheeled-legged mobile manipulation platform capable of executing demanding manipulation tasks, and demonstrating significant physical resilience while possessing a body size (height/width) and weight compatible to that of a human. The achieved performance is the result of combining a number of design and implementation principles related to the actuation system, the integration of body structure and actuation, and the wheeled-legged mobility concept. These design principles are discussed, and the solutions adopted for various robot components are detailed. Finally, the robot performance is demonstrated in a set of experiments validating its power and strength capability when manipulating heavy payload and executing tasks involving high impact physical interactions.

Fast Online Trajectory Optimization for the Bipedal Robot Cassie

Abstract: We apply fast online trajectory optimization for multi-step motion planning to Cassie, a bipedal robot designed to exploit natural spring-mass locomotion dynamics using lightweight, compliant legs. Our motion planning formulation simultaneously optimizes over center of mass motion, footholds, and center of pressure for a simplified model that combines transverse linear inverted pendulum and vertical spring dynamics. A vertex-based representation of the support area combined with this simplified dynamic model that allows closed form integration leads to a fast nonlinear programming problem formulation. This optimization problem is continuously solved online in a model predictive control approach. The output of the reduced-order planner is fed into a quadratic programming based operational space controller for execution on the full-order system. We present simulation results showing the performance and robustness to disturbances of the planning and control framework. Preliminary results on the physical robot show functionality of the operational space control system, with integration of the trajectory planner a work in progress.