Lift-off dynamics in a simple jumping robot

We study vertical jumping in a simple robot comprising an actuated mass-spring arrangement. The actuator frequency and phase are systematically varied to find optimal performance. Optimal jumps occur above and below (but not at) the robot's resonant frequency $f_0$. Two distinct jumping modes emerge: a simple jump which is optimal above $f_0$ is achievable with a squat maneuver, and a peculiar stutter jump which is optimal below $f_0$ is generated with a counter-movement. A simple dynamical model reveals how optimal lift-off results from non-resonant transient dynamics.Comment: 4 pages, 4 figures, Physical Review Letters, in press (2012

Passive Dynamics in the Control of Gymnastic Maneuvers

The control of aerial gymnastic maneuvers is challenging because these maneuvers frequently involve complex rotational motion and because the performer has limited control of the maneuver during flight. A performer can influence a manuever using a sequence of limbmovements during flight. However, the same sequence may not produce reliable performances in the presence of off-nominal conditions. Howdo people compensate for variations in performance to reliably produce aerial maneuvers? In this report I explore the role that passive dynamic stabilitymay play in making the performance of aerial maneuvers simple and reliable

Passive dynamics in the control of gymnastic maneuvers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1994.Includes bibliographical references (p. 174-177).by Robert R. Playter.Ph.D

Control system design using H [infinity] optimization

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1988.The bracketed word appears as the mathematical infinity symbol on the title page.Includes bibliographical references.by Robert Playter.M.S

Generating Life-like Motion for Robots

© The Author(s) 2013The definitive article is published in the International Journal of Robotics Research © Sage Publications. Located at: http://ijr.sagepub.com/content/early/2013/07/12/0278364913490533DOI: 10.1177/0278364913490533Action prediction and fluidity are key elements of human-robot teamwork. If a robot’s actions are hard to understand, it can impede fluid HRI. Our goal is to improve the clarity of robot motion by making it more humanlike. We present an algorithm that autonomously synthesizes human-like variants of an input motion. Our approach is a three stage pipeline. First we optimize motion with respect to spatio-temporal correspondence (STC), which emulates the coordinated effects of human joints that are connected by muscles. We present three experiments that validate that our STC optimization approach increases human-likeness and recognition accuracy for human social partners. Next in the pipeline, we avoid repetitive motion by adding variance, through exploiting redundant and underutilized spaces of the input motion, which creates multiple motions from a single input. In two experiments we validate that our variance approach maintains the human-likeness from the previous step, and that a social partner can still accurately recognize the motion’s intent. As a final step, we maintain the robot’s ability to interact with it’s world by providing it the ability to satisfy constraints. We provide experimental analysis of the effects of constraints on the synthesized human-like robot motion variants

Locomotion skills for simulated quadrupeds

International audienceWe develop an integrated set of gaits and skills for a physics-based simulation of a quadruped. The motion repertoire for our simulated dog includes walk, trot, pace, canter, transverse gallop, rotary gallop, leaps capable of jumping on-and-off platforms and over obstacles, sitting, lying down, standing up, and getting up from a fall. The controllers use a representation based on gait graphs, a dual leg frame model, a flexible spine model, and the extensive use of internal virtual forces applied via the Jacobian transpose. Optimizations are applied to these control abstractions in order to achieve robust gaits and leaps with desired motion styles. The resulting gaits are evaluated for robustness with respect to push disturbances and the traversal of variable terrain. The simulated motions are also compared to motion data captured from a filmed dog