From Human Walking to Bipedal Robot Locomotion

Abstract

Humans and other biological bipedal walkers are extraordinarily agile and robust. This is especially apparent when certain features of the environment are unknown such as unexpected downsteps (for example, suddenly walking off the side-walk or not expecting the last stair when descending). Humans can negotiate these downsteps ---both expected and unexpected--- with remarkable agility and ease. The contributions from active compensation, passive dynamics, and reflexes that humans employ to overcome these downsteps are however not inherently present in bipedal robots. This motivates us to assess whether externally observed behavior can be used to achieve the same capabilities in bipedal robots. One of the challenges with this proposal is the morphological differences between humans and robots. Taking the bipedal robot Cassie as an example, these differences predominantly constitute to overall- and segment mass, leg morphology, and a lack of an upper body. The observed behavior from the human should subsequently be scaled to represent an appropriate nominal and compensatory behavior of the robot.

This thesis aims to systematically study the translation of this human behavior to bipedal walking robots, regardless of the morphology of the walkers. We start from human experimental data where nominal walking, expected downstep, and unexpected downstep trials were conducted. The human data is analyzed from the perspective of a reduced-order representation of the human. The reduced-order representation encodes the center of mass dynamics and contact forces. An equivalent reduced-order model is used to represent the bipedal robot, which allows for the translation of the nominal walking and downstep behaviors between human and robot. The morphological differences between the human and the robot are therefore resolved by realizing dynamically equivalent behavior which is embedded into the full-order dynamics of a bipedal robot via optimization-based controllers. The results demonstrate traversing expected and unexpected downsteps in simulation on the underactuated 3D walking robot Cassie.