Mediating Between Contact Feasibility and Robustness of Trajectory Optimization Through Chance Complementarity...

Area of Science:

• Robotics
• Control Theory
• Artificial Intelligence

Background:

• Real-world robot deployment necessitates motion planning that addresses model uncertainty and risk.
• Intermittent contact motions are particularly vulnerable to uncertainty, where contact failures can be catastrophic.

Purpose of the Study:

• To develop a motion planning approach that accounts for uncertainty in terrain geometry and friction.
• To enable a trade-off between robustness to uncertainty and constraint satisfaction with high feasibility guarantees.

Main Methods:

• Modeling uncertainty in terrain geometry and friction characteristics.
• Combining a risk-sensitive objective with chance constraints.
• Evaluating the approach in a push-block system and a single-legged hopper.

Main Results:

• Chance constraints alone yield trajectories comparable to strict complementarity constraints.
• A risk-sensitive objective combined with chance constraints successfully mediates a trade-off between robustness and constraint satisfaction.
• The proposed method demonstrates improved handling of contact uncertainty.

Conclusions:

• This work presents a significant advancement in motion planning under contact uncertainty.
• The developed approach enhances robot reliability in real-world scenarios with unpredictable contact dynamics.
• Future research can build upon these findings for more complex robotic systems.