4 research outputs found
Optimal Control for Articulated Soft Robots
Soft robots can execute tasks with safer interactions. However, control
techniques that can effectively exploit the systems' capabilities are still
missing. Differential dynamic programming (DDP) has emerged as a promising tool
for achieving highly dynamic tasks. But most of the literature deals with
applying DDP to articulated soft robots by using numerical differentiation, in
addition to using pure feed-forward control to perform explosive tasks.
Further, underactuated compliant robots are known to be difficult to control
and the use of DDP-based algorithms to control them is not yet addressed. We
propose an efficient DDP-based algorithm for trajectory optimization of
articulated soft robots that can optimize the state trajectory, input torques,
and stiffness profile. We provide an efficient method to compute the forward
dynamics and the analytical derivatives of series elastic actuators
(SEA)/variable stiffness actuators (VSA) and underactuated compliant robots. We
present a state-feedback controller that uses locally optimal feedback policies
obtained from DDP. We show through simulations and experiments that the use of
feedback is crucial in improving the performance and stabilization properties
of various tasks. We also show that the proposed method can be used to plan and
control underactuated compliant robots, with varying degrees of underactuation
effectively.Comment: 14 pages, 15 figures, IEEE Transaction on Robotics (TRO
Inverse-Dynamics MPC via Nullspace Resolution
Optimal control (OC) using inverse dynamics provides numerical benefits such
as coarse optimization, cheaper computation of derivatives, and a high
convergence rate. However, in order to take advantage of these benefits in
model predictive control (MPC) for legged robots, it is crucial to handle its
large number of equality constraints efficiently. To accomplish this, we first
(i) propose a novel approach to handle equality constraints based on nullspace
parametrization. Our approach balances optimality, and both dynamics and
equality-constraint feasibility appropriately, which increases the basin of
attraction to good local minima. To do so, we then (ii) adapt our
feasibility-driven search by incorporating a merit function. Furthermore, we
introduce (iii) a condensed formulation of the inverse dynamics that considers
arbitrary actuator models. We also develop (iv) a novel MPC based on inverse
dynamics within a perception locomotion framework. Finally, we present (v) a
theoretical comparison of optimal control with the forward and inverse
dynamics, and evaluate both numerically. Our approach enables the first
application of inverse-dynamics MPC on hardware, resulting in state-of-the-art
dynamic climbing on the ANYmal robot. We benchmark it over a wide range of
robotics problems and generate agile and complex maneuvers. We show the
computational reduction of our nullspace resolution and condensed formulation
(up to 47.3%). We provide evidence of the benefits of our approach by solving
coarse optimization problems with a high convergence rate (up to 10 Hz of
discretization). Our algorithm is publicly available inside CROCODDYL.Comment: 17 pages, 14 figures, under-revie