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Humanoid Robots, 2007 7th IEEE-RAS International Conference on

Date Nov. 29 2007-Dec. 1 2007

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  • A collocation method for real-time walking pattern generation

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (330 KB) |  | HTML iconHTML  

    This paper presents a new real-time walking pattern generator that calculates center of mass trajectories from footstep locations. Key features are the calculation of reference torque patterns by quadratic programming and the solution of the equations of motion by spline collocation. Fast real-time planning is combined with offline optimisation of free parameters based on a comprehensive simulation of the closed loop system to automatically generate optimally tuned walking controllers. The effectiveness of the proposed method is shown using a dynamics simulation of the robot JOHNNIE. View full abstract»

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  • A strategy to combine active trajectory control with the exploitation of the natural dynamics to reduce energy consumption for bipedal robots

    Page(s): 7 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB) |  | HTML iconHTML  

    The biped Lucy, powered by pleated pneumatic artificial muscles, has been built and controlled and is able to walk up to a speed of 0.15 m/s. The pressures inside the muscles are controlled by a joint trajectory tracking controller to track the desired joint trajectories calculated by a trajectory generator. However, the actuators are set to a fixed stiffness value. In this paper a compliance controller is proposed which can be added in the control architecture to reduce the energy consumption by exploiting the natural dynamics. The goal of this research is to preserve the versatility of actively controlled humanoids, while reducing their energy consumption. A mathematical formulation has been developed to find an optimal stiffness setting depending on the desired trajectory and physical properties of the system and the proposed strategy has been validated on a pendulum structure powered by artificial muscles. This strategy has not been implemented on the real robot because the walking speed of the robot is currently too slow to benefit already from compliance control. View full abstract»

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  • Online gain switching algorithm for joint position control of a hydraulic humanoid robot

    Page(s): 13 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (758 KB) |  | HTML iconHTML  

    This paper proposes a gain switching algorithm for joint position control of a hydraulic humanoid robot. Accurate position control of the lower body is one of the basic requirements for robust balance and walking control. Joint position control is more difficult for hydraulic robots than it is for electric robots because of a slower actuator time constant and the back-drivability of hydraulic joints. Backdrivability causes external forces and torques to have a large effect on the position of the joints. External ground reaction forces therefore prevent a simple proportional-derivative (PD) controller from realizing accurate joint position control. We propose a state feedback controller for joint position control of the lower body, define three modes of state feedback gains, and switch the gains according to the zero moment point (ZMP) using linear interpolation. The performance of the algorithm is evaluated with a dynamic simulation of a hydraulic humanoid. View full abstract»

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  • Simultaneous adaptation to rough terrain and unknown external forces for biped humanoids

    Page(s): 19 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (719 KB) |  | HTML iconHTML  

    This paper reports on the applicability of our passivity-based contact force control framework for humanoids. We present its adaptation to unknown rough terrain. The adaptation to uneven ground is achieved by an optimally-distributed anti-gravitational forces applied to preset contact points in a feed-forward manner even without explicitly measuring the external forces or the terrain shape. The adaptation to unknown inclination is also possible by combining an active balancing controller based on the measurement of the CoM with respect to the inertial frame. Furthermore, it is shown that a simple impedance controller for the supporting feet or hands allows the robot to adapt to a low friction ground without prior knowledge of the ground friction. The proposed adaptive mechanism is experimentally validated on a full-size biped humanoid robot balancing on uneven ground or time-varying incline. View full abstract»

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  • A model of shared grasp affordances from demonstration

    Page(s): 27 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (624 KB) |  | HTML iconHTML  

    This paper presents a hierarchical, statistical topic model for representing the grasp preshapes of a set of objects. Observations provided by teleoperation are clustered into latent affordances shared among all objects. Each affordance defines a joint distribution over position and orientation of the hand relative to the object and conditioned on visual appearance. The parameters of the model are learned using a Gibbs sampling method. After training, the model can be used to compute grasp preshapes for a novel object based on its visual appearance. The model is evaluated experimentally on a set of objects for its ability to generate grasp preshapes that lead to successful grasps, and compared to a baseline approach. View full abstract»

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  • Backdrivable miniature hydrostatic transmission for actuation of anthropomorphic robot hands

    Page(s): 36 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (553 KB) |  | HTML iconHTML  

    Humanoid robots demand high performance on actuators, such as power-to-weight ratio, durability, occupation volume, and freedom of placement. Also, for robots to interact with unknown objects, flexibility is necessary. Backdrivability is effective when both flexibility and high output torque needs to be realized, which were often contradicting requirement. In this paper, backdrivable hydrostatic transmission is proposed to satisfy requirements above. First, development of hydrostatic actuator including conditions of backdrivability is explained. Second design of backdrivable pump, hydraulic motor and manifold design is explained. Next, design of an anthropomorphic robot hand using developed hydrostatic transmission is discussed. Finally, developed hand is presented and experiments on backdrivability, force sensing, and grasping were performed. View full abstract»

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  • Grasp planning in complex scenes

    Page(s): 42 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4179 KB) |  | HTML iconHTML  

    This paper combines grasp analysis and manipulation planning techniques to perform fast grasp planning in complex scenes. In much previous work on grasping, the object being grasped is assumed to be the only object in the environment. Hence the grasp quality metrics and grasping strategies developed do not perform well when the object is close to obstacles and many good grasps are infeasible. We introduce a framework for finding valid grasps in cluttered environments that combines a grasp quality metric for the object with information about the local environment around the object and information about the robot's kinematics. We encode these factors in a grasp-scoring function which we use to rank a precomputed set of grasps in terms of their appropriateness for a given scene. We show that this ranking is essential for efficient grasp selection and present experiments in simulation and on the HRP2 robot. View full abstract»

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  • Learning grasp strategies composed of contact relative motions

    Page(s): 49 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (315 KB) |  | HTML iconHTML  

    Of central importance to grasp synthesis algorithms are the assumptions made about the object to be grasped and the sensory information that is available. Many approaches avoid the issue of sensing entirely by assuming that complete information is available. In contrast, this paper focuses on the case where force feedback is the only source of new information and limited prior information is available. Although, in general, visual information is also available, the emphasis on force feedback allows this paper to focus on the partially observable nature of the grasp synthesis problem. In order to investigate this question, this paper introduces a parameterizable space of atomic units of control known as contact relative motions (CRMs). CRMs simultaneously displace contacts on the object surface and gather force feedback information relevant to the object shape and the relative manipulator-object pose. This allows the grasp synthesis problem to be re-cast as an optimal control problem where the goal is to find a strategy for executing CRMs that leads to a grasp in the shortest number of steps. Since local force feedback information usually does not completely determine system state, the control problem is partially observable. This paper expresses the partially observable problem as a k-order Markov Decision Process (MDP) and solves it using Reinforcement Learning. Although this approach can be expected to extend to the grasping of spatial objects, this paper focuses on the case of grasping planar objects in order to explore the ideas. The approach is tested in planar simulation and is demonstrated to work in practice using Robonaut, the NASA-JSC space humanoid. View full abstract»

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  • Multiple balance strategies from one optimization criterion

    Page(s): 57 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (475 KB) |  | HTML iconHTML  

    Multiple strategies for standing balance have been observed in humans, including using the ankles to apply torque to the ground, using the hips and/or arms to generate horizontal ground forces, and using the knees and hips to squat. This paper shows that multiple strategies can arise from the same optimization criterion. It is likely that humanoid robots will exhibit the same balance strategies as humans. View full abstract»

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  • Learning Capture Points for humanoid push recovery

    Page(s): 65 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3068 KB) |  | HTML iconHTML  

    We present a method for learning capture points for humanoid push recovery. A capture point is a point on the ground to which the biped can step and stop without requiring another step. Being able to predict the location of such points is very useful for recovery from significant disturbances, such as after being pushed. While dynamic models can be used to compute capture points, model assumptions and modeling errors can lead to stepping in the wrong place, which can result in large velocity errors after stepping.We present a method for computing capture points by learning offsets to the capture points predicted by the linear inverted pendulum model, which assumes a point mass biped with constant center of Mass height. We validate our method on a three dimensional humanoid robot simulation with 12 actuated lower body degrees of freedom, distributed mass, and articulated limbs. Using our learning approach, robustness to pushes is significantly improved as compared to using the linear inverted pendulum model without learning. View full abstract»

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