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Foundations of Robotics
Seminar, October 23, 2007
Time
and Place | Seminar Abstract1 | Seminar Abstract2 | Seminar
Abstract3 | Seminar Abstract4 | Speaker
Appointments
IROS 2007 Practice Talks
Smith Hall 100
4:00 pm
This paper presents a balance controller that allows
a humanoid to recover from large disturbances and still maintain an upright posture.
Balance is achieved by integral control, which decouples the dynamics and
produces smooth torque signals. Simulation shows the controller performs better
than other simple balance controllers. Because the controller is inspired by
human balance strategies, we compare human motion capture and force plate data
to simulation. A model tracking controller is also presented, making it
possible to control complex robots using this simple control.
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A Dynamic Single
Actuator Vertical Climbing Robot – Amir Degani |
A climbing robot mechanism is introduced, which uses
dynamic movements to climb between two parallel vertical walls. This robot
relies on its own internal dynamic motions to gain height, unlike previous
mechanisms which are quasistatic. One benefit of
dynamics is that it allows climbing with only a single actuated degree of
freedom. We show with analysis, simulations and experiments that this dynamic
robot is capable of climbing vertically between parallel walls. We introduce
simplifications that enable us to obtain closed form approximations of the
robot motion. Furthermore, this provides us with some design considerations and
insights into the mechanism’s ability to climb.
What would it be like if we could give our robot high level commands and it would automatically execute them in a verifiably correct fashion in dynamically changing environments? This work demonstrates a method for generating continuous feedback control inputs that satisfy high-level specifications. Using a collection of continuous local feedback control policies in concert with a synthesized discrete automaton, this paper demonstrates the approach on an Ackermann-steered vehicle that satisfies the command "drive around until you find an empty parking space, then park." The system reacts to changing environmental conditions using only local information, while guaranteeing the correct high level behavior. The local policies consider the vehicle body shape as well as bounds on drive and steering velocities. The discrete automaton that invokes the local policies guarantees executions that satisfy the high-level specification based only on information about the current availability of the nearest parking space. This paper also demonstrates coordination of two vehicles using the approach.
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Transfer of Policies
Based on Trajectory Libraries - Martin
Stolle |
Libraries of trajectories are a promising way of
creating policies for difficult problems.
However, often it is not desirable or even possible to create a new
library for every task. We present a
method for transferring libraries across tasks, which allows us to build
libraries by learning from demonstration on one task and apply them to similar
tasks. Representing the libraries in a
feature-based space is key to supporting
transfer. We also search through the
library to ensure a complete path to the goal is possible. Results are shown for the Little Dog task. Little Dog is a quadruped robot that has to
walk across rough terrain at reasonably fast speeds.
For appointments, please contact Amir
Degani.
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.