5 research outputs found
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
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
Contact Invariant Model Learning for Legged Robot Locomotion
In this work we present a new formulation for learning the dynamics of legged robots performing locomotion tasks. Using sensor data we learn error terms at the level of rigid body dynamics and actuation dynamics. The learning framework deals with the hybrid nature of legged systems given by different contact configurations: We use the projection of the rigid body dynamics into a subspace consistent with the contact constraints. The equations of motion in such subspace do not depend on the contact forces, allowing to formulate a learning problem where force sensor data is not required. Additionally, we propose to use the columns of end-effector Jacobians as basis vectors, obtaining a model that generalizes across contact configurations. Both Locally Weighted Projection Regression and Sparse Gaussian Process Regression are used as supervised learning techniques. As application of the learned model, an inverse dynamics control method is extended. Hardware experiments with a quadruped robot show reduced RMS tracking error and a significant reduction in RMS feedback effort during base-only, walking, and trotting motions.ISSN:2377-376