Joint Optimization of Robot Design and Motion Parameters using the Implicit Function Theorem


Sehoon Ha (Disney Research Pittsburgh)
Stelian Coros (Carnegie Mellon University)
Alexander Alspach (Disney Research Pittsburgh)
Joohyung Kim (Disney Research Pittsburgh)
Katsu Yamane (Disney Research Pittsburgh)

Robotics: Science and Systems 2017

July 12, 2017

We present a novel computational approach to optimizing the morphological design of robots. Our framework takes as input a parameterized robot design and a motion plan consisting of trajectories for end-effectors, as well as optionally, for its body. The algorithm we propose is used to optimize design parameters, namely link lengths and the placement of actuators, while concurrently adjusting motion parameters such as joint trajectories, actuator inputs, and contact forces. Our key insight is that the complex relationship between design and motion parameters can be established via sensitivity analysis if the robot’s movements are modeled as spatio-temporal solutions to optimal control problems. This relationship between form and function allows us to automatically optimize robot designs based on specifications expressed as a function of range of motion or actuator forces. We evaluate our model by computationally optimizing two simulated robots that employ linear actuators: a manipulator and a large quadruped. We further validate our framework by optimizing the design of a small quadrupedal robot and testing its performance using a hardware implementation.


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