Abstract:

Chance-Constrained Optimization for Contact-rich Systems using Mixed Integer Programming Yuki Shirai1, Devesh Jha2, Arvind U Raghunathan2, and Diego Romeres2 1Department of Mechanical and Aerospace Engineering, University of California Los Angeles, CA 90095, USA. email: yukishirai4869@g.ucla.edu 2Mitsubishi Electric Research Laboratories, 201 Broadway, Cambridge, MA 02139, USA. email: {jha,raghunathan,romeres}@merl.com Abstract Stochastic and robust optimization of uncertain contact-rich systems is relatively unexplored. This paper presents a chance-constrained formulation for robust trajec- tory optimization during manipulation. In particular, we present chance-constrained optimization of Stochastic Discrete-time Linear Complementarity Systems (SDLCS). The optimization problem is formulated as a Mixed-Integer Quadratic Program with Chance Constraints (MIQPCC). In our formulation, we explicitly consider joint chance constraints for complementarity variables and states to capture the stochastic evolu- tion of dynamics. Additionally, we demonstrate the use of our proposed approach for designing a Stochastic Model Predictive Controller (SMPC) with complementarity constraints for a planar pushing system. We evaluate the robustness of our optimized trajectories in simulation on several systems. The proposed approach outperforms some recent approaches for robust trajectory optimization for SDLCS.