TechTalks from event: Technical session talks from ICRA 2012

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Human Like Biped Locamotion

  • Regulating Speed and Generating Large Speed Transitions in a Neuromuscular Human Walking Model Authors: Song, Seungmoon; Geyer, Hartmut
    Although current humanoid controllers can rely on inverse kinematics or dynamics of the full humanoid system, powered prosthetic legs or assistive devices cannot, because they do not have access to the full states of the human system. This limitation creates the need for alternative control strategies. One strategy is to embed fundamental knowledge about legged dynamics and control in local feedback. In a previous paper, we have developed a control model of human locomotion which relies mostly on local feedback. The model can robustly walk at normal walking speeds. Here we extend this model to adapt to a wide range of walking speeds and to generate corresponding speed transitions. We use optimization of the model's control parameters and find key parameters responsible for steady walking between 0.8<i>ms<sup>-1</sup></i> and 1.8<i>ms<sup>-1</sup></i>, covering the range of speed at which humans normally walk. Using these parameters, we demonstrate speed transitions between the slow and fast walking. In addition, we discuss how the speed-dependent changes of the identified control parameters connect to biped walking dynamics, and suggest how these changes can be integrated in local feedback control.
  • Using Basin Ruins and Co-Moving Low-Dimensional Latent Coordinates for Dynamic Programming of Biped Walkers on Roughing Ground Authors: Suetani, Hiromichi; Ideta, Aiko; Morimoto, Jun
    Disturbance rejection is one of the most important abilities required for biped walkers. In this study, we propose a method for dynamic programming of biped walking and apply it to a simple passive dynamic walker (PDW) on an irregular slope. The key of the proposed approach is to employ the transient dynamics of the walker just before approaching the falling state in the absence of any controlling input, and to derive the optimal control policy in the low-dimensional latent space. In recent our study, we found that such transient dynamics deeply relates to the basin of attraction for a stable gait. By patching coordinates to such a structures in each Poincar¥'{e} surface and defining the reward function according to the survive time of the transient dynamics, we can construct a Markov Decision Process (MDP) for describing the PDW with external inputs, and we obtain optimal value and policy using a notion of dynamic programming (DP). We will show that the proposed method actually succeeds in controlling the PDW even if the degree of disturbance is relatively large and the dimensionality of coordinates is reduced to lower ones.
  • Spatio-temporal Synchronization of Periodic Movements by Style-phase Adaptation: Application to Biped Walking Authors: Matsubara, Takamitsu; Uchikata, Akimasa; Morimoto, Jun
    In this paper, we propose a framework for generating coordinated periodic movements of robotic systems with external inputs. We developed an adaptive pattern generator model that is composed of a two-factor observation model with style parameter and phase dynamics with a phase variable. The style parameter controls the spatial patterns of the generated trajectories, and the phase variable controls its temporal profiles. To validate the effectiveness of our proposed method, we applied it to a simulated humanoid model to perform biped walking behaviors coordinated with observed walking patterns and the environment. The robot successfully performed stable biped walking behaviors even when the style of the observed walking pattern and the period were suddenly changed.
  • A Convex Approach to Inverse Optimal Control and Its Application to Modeling Human Locomotion Authors: Puydupin-Jamin, Anne-Sophie; Johnson, Miles; Bretl, Timothy
    Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality. For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as a single quadratic program. We apply this approach to both simulated and experimental data to obtain a simple model of human walking paths. This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areas.
  • A Simple Bipedal Walking Model Reproduces Entrainment of Human Locomotion Authors: Ahn, Jooeun; Klenk, Daniel; Hogan, Neville
    Robotic studies have suggested a contribution of limit-cycle oscillation of the neuro-mechanical periphery to human walking by demonstrating stable bipedal robotic gaits with minimal actuation and control. As behavioral evidence of limit-cycle oscillation in human walking, we recently reported entrainment of human gaits to mechanical perturbations. We observed synchronization of human walking with mechanical perturbation only when the perturbation period was close to the original walking period. In addition, the entrainment was always accompanied by phase locking at the end of double-stance. A highly-simplified state-determined walker reproduced these salient features: 1) entrainment to periodic perturbations with a narrow basin of entrainment and 2) phase-locking at the end of double stance. Importantly, the model required neither supra-spinal control nor an intrinsic self-sustaining neural oscillator (like a rhythmic central pattern generator), which suggests that prominent features of human walking may stem from simple afferent feedback processes that produce limit-cycle oscillation of the neuro-mechanical periphery without significant involvement of the brain or rhythmic central pattern generators. One limitation of that model was that it entrained only to perturbations faster than the unperturbed walking period. In the study reported here, we modified the model to have two independent steps per stride. The revised model reproduced entrainment to perturbations both slower
  • Motion Primitives for Human-Inspired Bipedal Robotic Locomotion: Walking and Stair Climbing Authors: Powell, Matthew; Huihua, Zhao; Ames, Aaron
    This paper presents an approach to the development of bipedal robotic control techniques for multiple locomotion behaviors. Insight into the fundamental behaviors of human locomotion is obtained through the examination of experimental human data for walking on flat ground, upstairs and downstairs. Specifically, it is shown that certain outputs of the human, independent of locomotion terrain, can be characterized by a single function, termed the extended canonical human function. Optimized functions of this form are tracked via feedback linearization in simulations of a planar robotic biped walking on flat ground, upstairs and downstairs - these three modes of locomotion are termed &quot;motion primitives&quot;. A second optimization is presented, which yields controllers that evolve the robot from one motion primitive to another - these modes of locomotion are termed &quot;motion transitions&quot;. A final simulation is given, which shows the controlled evolution of a robotic biped as it transitions through each mode of locomotion over a pyramidal staircase.