Darwin G. Caldwell

A compact soft actuator unit for small scale human friendly robots

This paper presents the development of a new compact soft actuation unit intended to be used in multi degree of freedom and small scale robotic systems such as the child humanoid robot “iCub” [1]. Compared to the other existing series elastic linear or rotary implementations the proposed design shows high integration density and wider passive deflection. The miniaturization of the newly developed high performance unit was achieved with a use of a new rotary spring module based on a novel arrangement of linear springs.

Robot motor skill coordination with EM-based Reinforcement Learning

We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the users demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.

COMpliant huMANoid COMAN: Optimal joint stiffness tuning for modal frequency control

The incorporation of passive compliance in robotic systems could improve their performance during interactions and impacts, for energy storage and efficiency, and for general safety for both the robots and humans. This paper presents the recently developed COMpliant huMANoid COMAN. COMAN is actuated by passive compliance actuators based on the series elastic actuation principle (SEA). The design and implementation of the overall body of the robot is discussed including the realization of the different body segments and the tuning of the joint distributed passive elasticity. This joint stiffness tuning is a critical parameter in the performance of compliant systems. A novel systematic method to optimally tune the joint elasticity of multi-dof SEA robots based on resonance analysis and energy storage maximization criteria forms one of the key contributions of this work. The paper will show this method being applied to the selection of the passive elasticity of COMAN legs. The first completed robot prototype is presented accompanied by experimental walking trials to demonstrate its operation.

Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

A method to learn and reproduce robot force interactions in a human–robot interaction setting is proposed. The method allows a robotic manipulator to learn to perform tasks that require exerting forces on external objects by interacting with a human operator in an unstructured environment. This is achieved by learning two aspects of a task: positional and force profiles. The positional profile is obtained from task demonstrations via kinesthetic teaching. The force profile is obtained from additional demonstrations via a haptic device. A human teacher uses the haptic device to input the desired forces that the robot should exert on external objects during the task execution. The two profiles are encoded as a mixture of dynamical systems, which is used to reproduce the task satisfying both the positional and force profiles. An active control strategy based on task-space control with variable stiffness is then proposed to reproduce the skill. The method is demonstrated with two experiments in which the robot learns an ironing task and a door-opening task.

The design of the lower body of the compliant humanoid robot “cCub”

The “iCub ”is a robotic platform that was developed by the RobotCub [1] consortium to provide the cognition research community with an open “child-like ”humanoid platform for understanding and development of cognitive systems [1]. In this paper we present the mechanical realization of the lower body developed for the “cCub ”humanoid robot, a derivative of the original “iCub”, which has passive compliance in the major joints of the legs. It is hypothesized that this will give to the robot high versatility to cope with unpredictable disturbance ranging from small uneven terrain variations to unexpected collisions or even accidental falls. As part of the AMARSI European project, the passive compliance of this newly developed robot will be exploited for safer interaction, energy efficient and more aggressive damage-safe learning. The passive compliant actuation module used is a compact unit based on the series elastic actuator principle (SEA). In addition to the passive compliance the “cCub ”design includes other significant updates over the original prototype such as full joint state sensing including joint torque sensing and improved range of motion and torque capabilities. In this paper, the new leg mechanisms of the “cCub ”robot are introduced.

Statistical dynamical systems for skills acquisition in humanoids

Learning by imitation in humanoids is challenging due to the unpredictable environments these robots have to face during reproduction. Two sets of tools are relevant for this purpose: 1) probabilistic machine learning methods that can extract and exploit the regularities and important features of the task; and 2) dynamical systems that can cope with perturbation in real-time without having to replan the whole movement. We present a learning by imitation approach combining the two benefits. It is based on a superposition of virtual spring-damper systems to drive a humanoid robots movement. The method relies on a statistical description of the springs attractor points acting in different candidate frames of reference. It extends dynamic movement primitives models by formulating the dynamical systems parameters estimation problem as a Gaussian mixture regression problem with projection in different coordinate systems. The robot exploits local variability information extracted from multiple demonstrations of movements to determine which frames are relevant for the task, and how the movement should be modulated with respect to these frames. The approach is tested on the new prototype of the COMAN compliant humanoid with time-based and time-invariant movements, including bimanual coordination skills.

A passivity based admittance control for stabilizing the compliant humanoid COMAN

This paper presents a generic stabilization framework which is applicable for both compliant and stiff humanoids. The proposed control framework is applied to the passive compliant humanoid robot COMAN which is equipped with series elastic actuators. The stabilization control framework combines the compliance control and the intrinsic angular momentum modulation to achieve an agile and compliant interaction against external perturbations. The admittance based compliance control uses the force/torque sensing in both feet to regulate the active compliance for the position controlled system. The physical elasticity in the new full body COMAN is exploited for the reduction and absorption of the instantaneous impacts while the admittance control further dissipates the excessive elastic energy. The angular momentum controller reduces the overall inertia effect for providing more rapid reactions. Both the theoretical work and experimental validation were presented. The effectiveness of the control scheme is demonstrated by COMANs capabilities of withstanding various types of perturbations applied over the body, balancing on a moving platform and stabilizing while walking. Experimental data of the ground reaction force/torque, center of mass references and estimations, and the stored elastic energy are presented and analyzed.

Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies

We propose a control strategy for a robotic manipulator operating in an unstructured environment while interacting with a human operator. The proposed system takes into account the important characteristics of the task and the redundancy of the robot to determine a controller that is safe for the user. The constraints of the task are first extracted using several examples of the skill demonstrated to the robot through kinesthetic teaching. An active control strategy based on task-space control with variable stiffness is proposed, and combined with a safety strategy for tasks requiring humans to move in the vicinity of robots. A risk indicator for human-robot collision is defined, which modulates a repulsive force distorting the spatial and temporal characteristics of the movement according to the task constraints. We illustrate the approach with two human-robot interaction experiments, where the user teaches the robot first how to move a tray, and then shows it how to iron a napkin.

Stabilization for the compliant humanoid robot COMAN exploiting intrinsic and controlled compliance

The work presents the standing stabilization of a compliant humanoid robot against external force disturbances and variations of the terrain inclination. The novel contribution is the proposed control scheme which consists of three strategies named compliance control in the transversal plane, body attitude control, and potential energy control, all combined with the intrinsic passive compliance in the robot. The physical compliant elements of the robot are exploited to react at the first instance of the impact while the active compliance control is applied to further absorb the impact and dissipate the elastic energy stored in springs preventing the high rate of spring recoil. The body attitude controller meanwhile regulates the spin angular momentum to provide more agile reactions by changing body inclination. The potential energy control module constrains the robot center of mass (COM) in a virtual slope to convert the excessive kinetic energy into potential energy to prevent falling. Experiments were carried out with the proposed balance stabilization control demonstrating superior balance performance. The compliant humanoid was capable of recovering from external force disturbances and moderate or even abrupt variations of the terrain inclination. Experimental data such as the impulse forces, real COM, center of pressure (COP) and the spring elastic energy are presented and analyzed.

Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework

We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a time-independent model of a set of movements demonstrated by a human user. Two experiments are presented to validate the method, that consist of learning to hit a ball with a robotic arm, and of teaching a humanoid robot to manipulate a spoon to feed another humanoid. The experiments demonstrate that the proposed model can efficiently handle several aspects of learning by imitation. We first show that it can be utilized in an unsupervised learning manner, where the robot is autonomously organizing and encoding variants of motion from the multiple demonstrations. We then show that the approach allows to robustly generalize the observed skill by taking into account multiple constraints in task space during reproduction.