Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling

TL;DR

This paper formulates the complex task of robotic toss juggling as a trajectory optimization problem, enabling the control of up to 17 balls with high robustness on dual manipulators.

Contribution

It provides a detailed analysis of toss juggling, formalizes it as an optimization problem, and demonstrates real-time control reaching the theoretical limits of juggling complexity.

Findings

Achieved robust toss juggling with up to 17 balls.

Developed a trajectory optimization framework for dynamic juggling.

Validated the approach on a real-world robotic platform.

Abstract

Dynamic movements are ubiquitous in human motor behavior as they tend to be more efficient and can solve a broader range of skill domains than their quasi-static counterparts. For decades, robotic juggling tasks have been among the most frequently studied dynamic manipulation problems since the required dynamic dexterity can be scaled to arbitrarily high difficulty. However, successful approaches have been limited to basic juggling skills, indicating a lack of understanding of the required constraints for dexterous toss juggling. We present a detailed analysis of the toss juggling task, identifying the key challenges and formalizing it as a trajectory optimization problem. Building on our state-of-the-art, real-world toss juggling platform, we reach the theoretical limits of toss juggling in simulation, evaluate a resulting real-time controller in environments of varying difficulty and achieve robust toss juggling of up to 17 balls on two anthropomorphic manipulators.