Title
Receding-Horizon Perceptive Trajectory Optimization for Dynamic Legged Locomotion with Learned Initialization
Abstract
To dynamically traverse challenging terrain, legged robots need to continually perceive and reason about upcoming features, adjust the locations and timings of future footfalls and leverage momentum strategically. We present a pipeline that enables flexibly-parametrized trajectories for perceptive and dynamic quadruped locomotion to be optimized in an online, receding-horizon manner. The initial guess passed to the optimizer affects the computation needed to achieve convergence and the quality of the solution. We consider two methods for generating good guesses. The first is a heuristic initializer which provides a simple guess and requires significant optimization but is nonetheless suitable for adaptation to upcoming terrain. We demonstrate experiments using the ANYmaI C quadruped, with fully onboard sensing and computation, to crass obstacles at moderate speeds using this technique. Our second approach uses latent-mode trajectory regression (LMTR) to imitate expert data-while avoiding invalid interpolations between distinct behaviors such that minimal optimization is needed. This enables high-speed motions that make more expansive use of the robot's capabilities. We demonstrate it on flat ground with the real robot and provide numerical trials that progress toward deployment on terrain. These results illustrate a paradigm for advancing beyond short-horizon dynamic reactions, toward the type of intuitive and adaptive locomotion planning exhibited by animals and humans.
Year
DOI
Venue
2021
10.1109/ICRA48506.2021.9560794
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
DocType
Volume
Issue
Conference
2021
1
ISSN
Citations 
PageRank 
1050-4729
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Melon Oliwier111.76
Romeo Orsolino223.42
David Allen Surovik343.86
Mathieu Geisert412.44
Ioannis Havoutis511619.82
Maurice F. Fallon600.68