57 research outputs found
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Properly designing a system to exhibit favorable natural dynamics can greatly
simplify designing or learning the control policy. However, it is still unclear
what constitutes favorable natural dynamics and how to quantify its effect.
Most studies of simple walking and running models have focused on the basins of
attraction of passive limit-cycles and the notion of self-stability. We instead
emphasize the importance of stepping beyond basins of attraction. We show an
approach based on viability theory to quantify robust sets in state-action
space. These sets are valid for the family of all robust control policies,
which allows us to quantify the robustness inherent to the natural dynamics
before designing the control policy or specifying a control objective. We
illustrate our formulation using spring-mass models, simple low dimensional
models of running systems. We then show an example application by optimizing
robustness of a simulated planar monoped, using a gradient-free optimization
scheme. Both case studies result in a nonlinear effective stiffness providing
more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on
Robotics (2019
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace
Despite impressive results using reinforcement learning to solve complex
problems from scratch, in robotics this has still been largely limited to
model-based learning with very informative reward functions. One of the major
challenges is that the reward landscape often has large patches with no
gradient, making it difficult to sample gradients effectively. We show here
that the robot state-initialization can have a more important effect on the
reward landscape than is generally expected. In particular, we show the
counter-intuitive benefit of including initializations that are unviable, in
other words initializing in states that are doomed to fail.Comment: Proceedings of the 2018 IEEE International Conference on SImulation,
Modeling and Programming for Autonomous Robots (SIMPAR), Brisbane, Australia,
16-19 201
Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware
Learning instead of designing robot controllers can greatly reduce
engineering effort required, while also emphasizing robustness. Despite
considerable progress in simulation, applying learning directly in hardware is
still challenging, in part due to the necessity to explore potentially unstable
parameters. We explore the concept of shaping the reward landscape with
training wheels: temporary modifications of the physical hardware that
facilitate learning. We demonstrate the concept with a robot leg mounted on a
boom learning to hop fast. This proof of concept embodies typical challenges
such as instability and contact, while being simple enough to empirically map
out and visualize the reward landscape. Based on our results we propose three
criteria for designing effective training wheels for learning in robotics. A
video synopsis can be found at https://youtu.be/6iH5E3LrYh8.Comment: Accepted to the IEEE International Conference on Robotics and
Automation (ICRA) 2018, 6 pages, 6 figure
FootTile: a Rugged Foot Sensor for Force and Center of Pressure Sensing in Soft Terrain
In this paper we present FootTile, a foot sensor for reaction force and
center of pressure sensing in challenging terrain. We compare our sensor design
to standard biomechanical devices, force plates and pressure plates. We show
that FootTile can accurately estimate force and pressure distribution during
legged locomotion. FootTile weighs 0.9g, has a sampling rate of 330Hz, a
footprint of 10 by 10mm and can easily be adapted in sensor range to the
required load case. In three experiments we validate: first the performance of
the individual sensor, second an array of FootTiles for center of pressure
sensing and third the ground reaction force estimation during locomotion in
granular substrate. We then go on to show the accurate sensing capabilities of
the waterproof sensor in liquid mud, as a showcase for real world rough terrain
use
Trunk Pitch Oscillations for Energy Trade-offs in Bipedal Running Birds and Robots
Bipedal animals have diverse morphologies and advanced locomotion abilities.
Terrestrial birds, in particular, display agile, efficient, and robust running
motion, in which they exploit the interplay between the body segment masses and
moment of inertias. On the other hand, most legged robots are not able to
generate such versatile and energy-efficient motion and often disregard trunk
movements as a means to enhance their locomotion capabilities. Recent research
investigated how trunk motions affect the gait characteristics of humans, but
there is a lack of analysis across different bipedal morphologies. To address
this issue, we analyze avian running based on a spring-loaded inverted pendulum
model with a pronograde (horizontal) trunk. We use a virtual point based
control scheme and modify the alignment of the ground reaction forces to assess
how our control strategy influences the trunk pitch oscillations and energetics
of the locomotion. We derive three potential key strategies to leverage trunk
pitch motions that minimize either the energy fluctuations of the center of
mass or the work performed by the hip and leg. We suggest how these strategies
could be used in legged robotics.Comment: 16 pages, 18 figures, accepted manuscript, online since 12 February
2020, published by IOP Publishing Lt
Effective Viscous Damping Enables Morphological Computation in Legged Locomotion
Muscle models and animal observations suggest that physical damping is
beneficial for stabilization. Still, only a few implementations of mechanical
damping exist in compliant robotic legged locomotion. It remains unclear how
physical damping can be exploited for locomotion tasks, while its advantages as
sensor-free, adaptive force- and negative work-producing actuators are
promising. In a simplified numerical leg model, we studied the energy
dissipation from viscous and Coulomb damping during vertical drops with
ground-level perturbations. A parallel spring-damper is engaged between
touch-down and mid-stance, and its damper auto-disengages during mid-stance and
takeoff. Our simulations indicate that an adjustable and viscous damper is
desired. In hardware we explored effective viscous damping and adjustability
and quantified the dissipated energy. We tested two mechanical, leg-mounted
damping mechanisms; a commercial hydraulic damper, and a custom-made pneumatic
damper. The pneumatic damper exploits a rolling diaphragm with an adjustable
orifice, minimizing Coulomb damping effects while permitting adjustable
resistance. Experimental results show that the leg-mounted, hydraulic damper
exhibits the most effective viscous damping. Adjusting the orifice setting did
not result in substantial changes of dissipated energy per drop, unlike
adjusting damping parameters in the numerical model. Consequently, we also
emphasize the importance of characterizing physical dampers during real legged
impacts to evaluate their effectiveness for compliant legged locomotion
Towards Pitch-Free Control of an Underactuated and Compliant Bipedal Robot
The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P7
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