🤖 AI Summary
This work addresses the challenges of satisfying terminal constraints and achieving high sample efficiency in long-horizon stochastic trajectory optimization. The authors propose a stochastic multi-segment shooting method that stitches together short action sequences optimized under local feedback policies. By leveraging trajectory-based Jacobian approximations—without requiring explicit model gradients—the approach significantly enhances convergence to the terminal set and improves sample efficiency. Framed as a novel paradigm for model-based reinforcement learning under black-box dynamics, the method demonstrates superior performance over existing approaches across three nonlinear underactuated tasks, encompassing both analytical and neural network-based dynamics models, thereby validating its effectiveness in meeting terminal constraints and reducing sample complexity.
📝 Abstract
Stochastic single shooting trajectory optimization methods such as Model Predictive Path Integral control (MPPI) have been widely adopted in robotics due to their ability to reason about probabilistic dynamics and provide solutions where model gradients are noisy, costly to evaluate, or unavailable. However, satisfaction of terminal constraints when shooting over long action sequences is often sample inefficient, requiring a large number of iterations for convergence. In this paper, we present a stochastic multiple shooting method that optimizes short control action sequences connected via local feedback policies to improve sample efficiency and convergence to a terminal set. Additionally, we show that we are able to synthesize approximate system Jacobians purely from rollouts, making the method suitable for model-based reinforcement learning with black-box dynamics. We demonstrate the algorithm has improved sample efficiency and terminal set convergence for three nonlinear, underactuated optimization problems: a classic cartpole swingup task with analytical dynamics, a cartpole swingup task with learned neural network dynamics, and a VTOL quadplane performing a high angle-of-attack, precision post-stall landing maneuver.