Gait generation for soft robots is challenging due to the nonlinear dynamics
and high dimensional input spaces of soft actuators. Limitations in soft
robotic control and perception force researchers to hand-craft open loop
controllers for gait sequences, which is a non-trivial process. Moreover, short
soft actuator lifespans and natural variations in actuator behavior limit
machine learning techniques to settings that can be learned on the same time
scales as robot deployment. Lastly, simulation is not always possible, due to
heterogeneity and nonlinearity in soft robotic materials and their dynamics
change due to wear. We present a sample-efficient, simulation free, method for
self-generating soft robot gaits, using very minimal computation. This
technique is demonstrated on a motorized soft robotic quadruped that walks
using four legs constructed from 16 ``handed shearing auxetic" (HSA) actuators.
To manage the dimension of the search space, gaits are composed of two
sequential sets of leg motions selected from 7 possible primitives. Pairs of
primitives are executed on one leg at a time; we then select the
best-performing pair to execute while moving on to subsequent legs. This method
-- which uses no simulation, sophisticated computation, or user input --
consistently generates good translation and rotation gaits in as low as 4
minutes of hardware experimentation, outperforming hand-crafted gaits. This is
the first demonstration of completely autonomous gait generation in a soft
robot.

使用非常少的计算，无需模拟和用户输入的方法，实现了软体机器人的自动生成步态，性能优于手工制作的步态。

步態生成自動化方法用於行走的軟性機器四足

Automated Gait Generation For Walking, Soft Robotic Quadrupeds

Stable gait generation is a crucial problem for legged robot locomotion as
this impacts other critical performance factors such as, e.g. mobility over an
uneven terrain and power consumption. Gait generation stability results from
the efficient control of the interaction between the legged robot's body and
the environment where it moves. Here, we study how this can be achieved by a
combination of model-predictive and predictive reinforcement learning
controllers. Model-predictive control (MPC) is a well-established method that
does not utilize any online learning (except for some adaptive variations) as
it provides a convenient interface for state constraints management.
Reinforcement learning (RL), in contrast, relies on adaptation based on pure
experience. In its bare-bone variants, RL is not always suitable for robots due
to their high complexity and expensive simulation/experimentation. In this
work, we combine both control methods to address the quadrupedal robot stable
gate generation problem. The hybrid approach that we develop and apply uses a
cost roll-out algorithm with a tail cost in the form of a Q-function modeled by
a neural network; this allows to alleviate the computational complexity, which
grows exponentially with the prediction horizon in a purely MPC approach. We
demonstrate that our RL gait controller achieves stable locomotion at short
horizons, where a nominal MP controller fails. Further, our controller is
capable of live operation, meaning that it does not require previous training.
Our results suggest that the hybridization of MPC with RL, as presented here,
is beneficial to achieve a good balance between online control capabilities and
computational complexity.

本文研究了基于模型预测控制和强化学习控制器相结合的四足机器人稳定步态生成问题，并开发了一种融合了这两种方法的混合控制方法，其中采用一个以神经网络建模的 Q 函数形式的尾部成本算法来降低计算复杂度，并证明了我们的控制器在短时间内能够实现稳定步态，具有实时操作的能力。