Contact-Implicit Trajectory Optimization with Hydroelastic Contact and iLQR
This work addresses scalability issues in generating contact-rich behaviors for robotics, though it appears incremental by combining existing methods.
The paper tackles the challenge of ensuring numerical reliability and physical realism in contact-implicit trajectory optimization for robot manipulation and locomotion, achieving physically accurate trajectories as demonstrated by open-loop playback on a Kinova Gen3 robot arm.
Contact-implicit trajectory optimization offers an appealing method of automatically generating complex and contact-rich behaviors for robot manipulation and locomotion. The scalability of such techniques has been limited, however, by the challenge of ensuring both numerical reliability and physical realism. In this paper, we present preliminary results suggesting that the Iterative Linear Quadratic Regulator (iLQR) algorithm together with the recently proposed pressure-field-based hydroelastic contact model enables reliable and physically realistic trajectory optimization through contact. We use this approach to synthesize contact-rich behaviors like quadruped locomotion and whole-arm manipulation. Furthermore, open-loop playback on a Kinova Gen3 robot arm demonstrates the physical accuracy of the whole-arm manipulation trajectories. Code is available at https://bit.ly/ilqr_hc and videos can be found at https://youtu.be/IqxJKbM8_ms.