Robust Nonprehensile Object Transportation with Uncertain Inertial Parameters
Adam Heins, Angela P. Schoellig
AI summary
Problem
Existing nonprehensile transportation planners ignore or poorly handle uncertainty in an object's mass and inertia distribution, causing robots to drop tall or unwieldy items during fast movement.
Approach
The authors integrate robust sticking constraints into an optimization-based motion planner and use moment relaxations to verify that trajectories remain safe for any physically realizable inertial parameters.
Key results
- Robust motion planner explicitly handling uncertain center-of-mass and inertia
- Theoretical verification of sticking constraints via moment relaxations and semidefinite programming
- Successful real-world transport of a 56 cm tall object under high inertial uncertainty
- Open-source implementation of the proposed planner
Why it matters
Enables reliable nonprehensile manipulation for service and warehouse robots handling objects with unknown or shifting internal compositions.
Abstract
We consider the nonprehensile object transportation task known as the waiter’s problem—in which a robot must move an object on a tray from one location to another—when the transported object has uncertain inertial parameters. In contrast to existing approaches that completely ignore uncertainty in the inertia matrix or which only consider small parameter errors, we are interested in pushing the limits of the amount of inertial parameter uncertainty that can be handled. We first show how constraints that are robust to inertial parameter uncertainty can be incorporated into an optimization-based motion planning framework to transport objects while moving quickly. Next, we develop necessary conditions for the inertial parameters to be realizable on a bounding shape based on moment relaxations, allowing us to verify whether a trajectory will violate the constraints for any realizable inertial parameters. Finally, we demonstrate our approach on a mobile manipulator in simulations and real hardware experiments: our proposed robust constraints consistently successfully transport a 56 cm tall object with substantial inertial parameter uncertainty in the real world, while the baseline approaches drop the object while transporting it.