Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
Conference registration code to access these videos can be accessed by visiting this link: PaperPlaza. Step-by-step to access these videos are here: step-by-step process .
Why some of the videos are missing? If you had provided your consent form for your video to be published and still it is missing, please contact firstname.lastname@example.org
Humanoid Motion Planning and Control
Controlling the Planar Motion of a Heavy Object by Pushing with a Humanoid Robot Using Dual-Arm Force ControlPushing heavy and large objects in a plane requires generating correct operational forces that compensate for unpredictable ground-object friction forces. This is a challenge because the reaction forces from the heavy object can easily cause a humanoid robot to slip at its feet or lose balance and fall down. Although previous research has addressed humanoid robot balancing problems to prevent falling down while pushing an object, there has been little discussion about the problem of avoiding slipping due to the reaction forces from the object. We extend a full-body balancing controller by simultaneously controlling the reaction forces of both hands using dual-arm force control. The main contribution of this paper is a method to calculate dual-arm reference forces considering the moments around the vertical axis of the humanoid robot and objects. This method involves estimating friction forces based on force measurements and controlling reaction forces to follow the reference forces. We show experimental results on the HRP-2 humanoid robot pushing a 90[kg] wheelchair.
Hopping at the Resonance Frequency: A Pattern Generation Technique for Bipedal Robots with Elastic JointsIt is known that bipedal robots with passive compliant structures have obvious advantages over stiff robots, as they are able to handle the potential energy management. Therefore, this paper is aimed at presenting a jumping pattern generation method that takes advantage of this property via the utilization of the ankle joint resonance frequency, which is of special importance. To begin with, the resonance frequency is determined through a system identification procedure on our actual robot. Consequentially, the vertical component of the CoM is generated via a periodic function in which the resonance frequency is employed. The horizontal component of the CoM is obtained using the ZMP criterion to guarantee the dynamic balance. Having analytically generated the necessary elements of the CoM trajectory, joint motions are computed with the help of translational and angular momenta constraints. In order to validate the method, two legged jumping experiments are conducted on our actual compliant robot. In conclusion, we satisfactorily observed repetitive, continuous, and dynamically equilibrated jumping cycles with successful landing phases.
Humanoid Motion Optimization Via Nonlinear Dimension ReductionThis paper examines the extent to which nonlinear dimension reduction techniques from machine learning can be exploited to determine dynamically optimal motions for high degree-of-freedom systems. Using the Gaussian Process Latent Variable Model (GPLVM) to learn the low-dimensional embedding, and a density function that provides a nonlinear mapping from the low-dimensional latent space to the full-dimensional pose space, we determine optimal motions by optimizing the latent space, and mapping the optimal trajectory in the latent space to the pose space. The notion of variance tubes are developed to ensure that kinematic constraints and other are appropriately satisfied without sacrificing naturalness or richness of the motions. Case studies of a 62-dof humanoid performing two sports motions---a golf swing and throwing a baseball---demonstrate that our method can be a highly effective, computationally efficient method for generating dynamically optimal motions.
A Neurorobotic Model of Bipedal Locomotion Based on Principles of Human Neuromuscular ArchitectureIn this paper, we present a walking biped, based on principles of mammalian neuromuscular architecture. Walking in mammals is a fluid, dynamical interaction between a central pattern generator, the biomechanics of the body, the environment, and sensory feedback. Our robot is designed based on principles of human leg muscle architecture. We incorporate load detecting force sensors that model Golgi tendon organs in the muscles, as well as foot pressure and joint angle sensors. These sensory feedback sources model those available in the human body. The robot is controlled by a spiking neuron simulation that integrates centrally generated (CPG) with peripheral (reflexive) responses. Using recent understanding of the neurobiology of locomotion, we are able to generate an effective and stable walking pattern using interactions between the biomechanics, CPG, and reflexive responses. The CPG drives overall limb motion at the hips, while phase modulated reflexive responses adapt the pattern of the lower limb to the needs of the step cycle. Load detection by the force sensors in the limb generates propulsive stepping, and controls entrainment of the CPG through positive force feedback. These concepts are important ones for locomotion in mammals that should be considered by roboticists developing walking robots.
Walking Control of Fully Actuated Robots Based on the Bipedal SLIP ModelThe goal of this paper is to generate and stabilize a periodic walking motion for a five degrees of freedom planar robot. First of all we will consider a biped version of the spring loaded inverted pendulum (SLIP), which shows openloop stable behavior. Then we will control the robot behavior as close as possible to the simple model. In this way we take advantage of the open-loop stability of the walking pattern related to the SLIP, and additional control actions are used to increase the robustness of the system and reject external disturbances. To this end an upper level controller will deal with the stabilization of the SLIP model, while a lower level controller will map the simple virtual model onto the real robot dynamics. Two different approaches are implemented for the lower level: in the first one, we aim at exactly reproducing the same acceleration that a SLIP would have when put in the same condition, while in the second one, we aim at a simpler control law without exactly reproducing the aforementioned acceleration. The latter case is equivalent to considering a SLIP with additional external disturbances, which have to be handled by the upper level controller. Both approaches can successfully reproduce a periodic walking pattern for the robot.
Muscle Force Transmission to Operational Space Accelerations During Elite Golf SwingsThe paper investigates the dynamic characteristics that shape human skills using the task-space methods found in robotics research. It is driven by the hypothesis that each subject's physiology can be reflected to the task dynamics using the operational space acceleration characteristics and that elite performers achieve the optimum transmission from their available muscle induced torque capacity to the desired task in goal oriented dynamic skills. The methodology is presented along with the full body human musculoskeletal model used for the task-based analyzes. The robotics approach for human motion characterization is demonstrated in the biomechanical analysis of an elite golf swing. This approach allows us to trace the acceleration capacities in a given subject's task space. The results of the motion characterization show that humans in fact follow a path of trajectory in line with the maximum available operational space accelerations benefiting from their physiology shaped by the combination of the force generating capacities of the muscles as well as by the joint and limb mechanics.