Covariance Steering for Uncertain Contact-rich Systems
arXiv:2303.13382 · doi:10.1109/ICRA48891.2023.10160249
Abstract
Planning and control for uncertain contact systems is challenging as it is not clear how to propagate uncertainty for planning. Contact-rich tasks can be modeled efficiently using complementarity constraints among other techniques. In this paper, we present a stochastic optimization technique with chance constraints for systems with stochastic complementarity constraints. We use a particle filter-based approach to propagate moments for stochastic complementarity system. To circumvent the issues of open-loop chance constrained planning, we propose a contact-aware controller for covariance steering of the complementarity system. Our optimization problem is formulated as Non-Linear Programming (NLP) using bilevel optimization. We present an important-particle algorithm for numerical efficiency for the underlying control problem. We verify that our contact-aware closed-loop controller is able to steer the covariance of the states under stochastic contact-rich tasks.
arXiv commentAccepted to the 2023 International Conference on Robotics and Automation (ICRA2023)
References in corpus (5)
- Risk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming
- Particle Model Predictive Control: Tractable Stochastic Nonlinear Output-Feedback MPC
- Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots
- Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization
- Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation
Cited by in corpus (5)
- Tactile Tool Manipulation
- Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization
- Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
- Maximum Entropy Density Control of Discrete-Time Linear Systems with Quadratic Cost
- Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations