28 research outputs found
Optimal gait and form for animal locomotion
We present a fully automatic method for generating gaits and morphologies for legged animal locomotion. Given a specific animal’s shape we can determine an efficient gait with which it can move. Similarly, we can also adapt the animal’s morphology to be optimal for a specific locomotion task. We show that determining such gaits is possible without the need to specify a good initial motion, and without manually restricting the allowed gaits of each animal. Our approach is based on a hybrid optimization method which combines an efficient derivative-aware spacetime constraints optimization with a derivative-free approach able to find non-local solutions in high-dimensional discontinuous spaces. We demonstrate the effectiveness of this approach by synthesizing dynamic locomotions of bipeds, a quadruped, and an imaginary five-legged creature
Co-Designing Robots by Differentiating Motion Solvers
We present a novel algorithm for the computational co-design of legged robots
and dynamic maneuvers. Current state-of-the-art approaches are based on random
sampling or concurrent optimization. A few recently proposed methods explore
the relationship between the gradient of the optimal motion and robot design.
Inspired by these approaches, we propose a bilevel optimization approach that
exploits the derivatives of the motion planning sub-problem (the inner level)
without simplifying assumptions on its structure. Our approach can quickly
optimize the robot's morphology while considering its full dynamics, joint
limits and physical constraints such as friction cones. It has a faster
convergence rate and greater scalability for larger design problems than
state-of-the-art approaches based on sampling methods. It also allows us to
handle constraints such as the actuation limits, which are important for
co-designing dynamic maneuvers. We demonstrate these capabilities by studying
jumping and trotting gaits under different design metrics and verify our
results in a physics simulator. For these cases, our algorithm converges in
less than a third of the number of iterations needed for sampling approaches,
and the computation time scales linearly.Comment: 8 pages, 7 figures, submitted to IROS 202