Abstract: This report describes our approach for Phase 3 of the Real Robot Challenge. To solve cuboid manipulation tasks of varying difficulty, we decompose each task into the following primitives: moving the fingers to the cuboid to grasp it, turning it on the table to minimize orientation error, and re-positioning it to the goal position. We use model-based trajectory optimization and control to plan and execute these primitives. These grasping, turning, and re-positioning primitives are sequenced with a state-machine that determines which primitive to execute given the current object state and goal. Our method shows robust performance over multiple runs with randomized initial and goal positions. With this approach, our team placed second in the challenge, under the anonymous name “sombertortoise” on the leaderboard. Example runs of our method solving each of the four levels can be seen in this video.
[1]
Ludovic Righetti,et al.
TriFinger: An Open-Source Robot for Learning Dexterity
,
2020,
CoRL.
[2]
Allison M. Okamura,et al.
An overview of dexterous manipulation
,
2000,
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).
[3]
Lorenz T. Biegler,et al.
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
,
2006,
Math. Program..
[4]
Alec Radford,et al.
Proximal Policy Optimization Algorithms
,
2017,
ArXiv.