On the dynamics of human locomotion and co-design of lower limb assistive devices

Recent developments in lower extremities wearable robotic devices for the assistance and rehabilitation of humans suffering from an impairment have led to several successes in the assistance of people who as a result regained a certain form of locomotive capability. Such devices are conventionally designed to be anthropomorphic. They follow the morphology of the human lower limbs. It has been shown previously that non-anthropomorphic designs can lead to increased comfort and better dynamical properties due to the fact that there is more morphological freedom in the design parameters of such a device. At the same time, exploitation of this freedom is not always intuitive and can be difficult to incorporate. In this work we strive towards a methodology aiding in the design of possible non-anthropomorphic structures for the task of human locomotion assistance by means of simulation and optimization. The simulation of such systems requires state of the art rigid body dynamics, contact dynamics and, importantly, closed loop dynamics. Through the course of our work, we first develop a novel, open and freely available, state of the art framework for the modeling and simulation of general coupled dynamical systems and show how such a framework enables the modeling of systems in a novel way. The resultant simulation environment is suitable for the evaluation of structural designs, with a specific focus on locomotion and wearable robots. To enable open-ended co-design of morphology and control, we employ population-based optimization methods to develop a novel Particle Swarm Optimization derivative specifically designed for the simultaneous optimization of solution structures (such as mechanical designs) as well as their continuous parameters. The optimizations that we aim to perform require large numbers of simulations to accommodate them and we develop another open and general framework to aid in large scale, population based optimizations in multi-user environments. Using the developed tools, we first explore the occurrence and underlying principles of natural human gait and apply our findings to the optimization of a bipedal gait of a humanoid robotic platform. Finally, we apply our developed methods to the co-design of a non-anthropomorphic, lower extremities, wearable robot in simulation, leading to an iterative co-design methodology aiding in the exploration of otherwise hard to realize morphological design

Automatic Gait Generation in Modular Robots: to Oscillate or to Rotate? that is the question

Modular robots offer the possibility to design robots with a high diversity of shapes and functionalities. This nice feature also brings an important challenge: namely how to design efficient locomotion gaits for arbitrary robot structures with many degrees of freedom. In this paper, we present a framework that allows one to explore and identify highly different gaits for a given arbitraryshaped modular robot. For this, we use simulated robots made of several Roombots modules that have three rotational joints each. These modules have as interesting feature that they can produce both oscillatory movements (i.e. periodic movements around a rest position) and rotational movements (i.e. with continuously increasing angle), leading to very rich locomotion patterns. Here we ask ourselves which types of movements — purely oscillatory, purely rotational, or a combination of both— lead to the fastest gaits. To address this question we designed a control architecture based on a distributed system of coupled phase oscillators that can produce synchronized rotations and oscillations in many degrees of freedom. We also designed a specific optimization algorithm that can automatically design hybrid controllers, i.e. controllers that use oscillations in some joints and rotations in others, for fast gaits. The proposed framework is verified through multiple simulations for several robot morphologies. The results show that (i) the question whether it is better to oscillate or to rotate depends on the morphology of the robot, and that in general it is best to do both, (ii) the optimization framework can successfully generate hybrid controllers that outperform purely oscillatory and purely rotational ones, and (iii) the resulting gaits are fast, innovative, and would have been hard to design by han

Model-Based and Model-Free Approaches for Postural Control of a Compliant Humanoid Robot using Optical Flow

Vision is a very rich sensor with a proven critical role in the control of balance. However, it is widely under- used for robotics postural control. This paper presents and compares two approaches, one model-based and one model-free, to ensure stability of the COMAN compliant humanoid robot standing on a moving platform. The model-based approach uses inverse kinematics, while the model-free one relies on a neural network as mapping between sensors and actuators. The sensory information is composed of proprioceptive cues (gyroscope) and visual cues, used separately or together. We present methods of using vision as sensory input without relying on a particular object or feature of the scene, but only on the optical flow. The performance of both approaches are compared systematically in a realistic robotics simulator, for different movements of the platform and using different sensory cues. We aim to see if vision can replace proprioceptive sensors or be fused with them to improve the performance of the stabilizing controller. While both model-based and model-free approaches successfully stabilize the robot, the model-free approach shows better overall performance. Preliminary results on the real COMAN robot are show

Real-time full body motion imitation on the COMAN humanoid robot

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Just as their discrete counterparts, periodic or rhythmic dynamic motion primitives allow easily modulated and robust motion generation, but for periodic tasks. In this paper we present an approach for modulating periodic dynamic movement primitives based on force feedback, allowing for rich motor behavior and skills. We propose and evaluate the combination of feedback and learned feed-forward terms to fully adapt the motions of a robot in order to achieve a desired force interaction with the environment. For the learning we employ the notion of repetitive control, which can effectively minimize the error of behavior towards a given reference. To demonstrate the approach, we show results of simulated and real world experiments on a compliant humanoid robot COMAN. We show the initial results of utilizing the approach to control a pedal-racer, a demanding balance toy best described as a hybrid between a skateboard and a bicycle. © 2014 IEEE

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot's own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibratio

Automatic Generation of Reduced CPG Control Networks for Locomotion of Arbitrary Modular Robot Structures

The design of efficient locomotion controllers for arbitrary structures of reconfigurable modular robots is challenging because the morphology of the structure can change dynamically during the completion of a task. In this paper, we propose a new method to automatically generate reduced Central Pattern Generator (CPG) networks for locomotion control based on the detection of bio-inspired sub-structures, like body and limbs, and articulation joints inside the robotic structure. We demonstrate how that information, coupled with the potential symmetries in the structure, can be used to speed up the optimization of the gaits and investigate its impact on the solution quality (i.e. the velocity of the robotic structure and the potential internal collisions between robotic modules). We tested our approach on three simulated structures and observed that the reduced network topologies in the first iterations of the optimization process performed significantly better than the fully open ones

Co-evolution of Morphology and Control of a Wearable Robot for Human Locomotion Assistance Exploiting Variable Impedance Actuators

The basic idea underlying this research is that the performances of wearable robots (WR) might be improved by optimizing the dynamics of the system comprised of the robot and the human body wearing it. This problem is not amenable to analytical investigations, and it asks for suitable numerical techniques able to simultaneously account for both robot mechanical structure dynamics and control laws. This paper presents on-going research efforts oriented to demonstrate a novel methodology for the design of an active lower limbs orthosis

Exploiting Natural Dynamics in Biped Locomotion using Variable Impedance Control

Biped locomotion has been extensively studied using various approaches, including Passive Dynamic Walking, ZMP-based control and biologically inspired muscle models. In biped locomotion robotics, there has been an increasing interest in variable impedance actuation/control as opposed to stiff, position-based control. Although primarily researched in the context of human-robot interaction, it has also been shown to play an important role in human locomotion. In this work we explore the use of variable impedance control for biped locomotion at the joint level. We hypothesize that 1) human like gait emerges from model-free optimization of first principles only, 2) joint variable impedance control increases gait quality and 3) variable impedance decreases energy expenditure. These hypotheses are explored in 2D using a simple humanoid model and Particle Swarm Optimization to optimize controllers that minimize energy expenditure. Our results confirm our first hypothesis and show a trend towards the second and third hypotheses

Meta Morphic Particle Swarm Optimization

Particle Swarm Optimization is a simple and elegant optimization algorithm used to solve a large variety of different real-valued problems. When it comes to solving combinations of continuous and discrete problems however, PSO by itself is not very well suited for the task. There have been previous works addressing the issue of solving solely discrete problems with PSO, but solving problems involving both discrete and continuous parameters at the same time with a PSO-like algorithm has not yet been fully explored. In this paper we provide a novel PSO-based algorithm, called Meta Morphic Particle Swarm Optimization, which looks at solving a particular class of problems for which there exists a discrete set of possible ways to solve the problem where each possibility uses a different subset of a continuous, real-valued parameter space. We introduce a two-layered approach, a PSO in the inner layer for the continuous space, and an outer layer, guided migration scheme using probabilities to choose between the different possible solution sets. We analyze the performance and characteristics of this new algorithm and show how it can be used for real-world applications