Throwing Planning Diffusion: A Solution to Learning and Planning of Robotic Throwing

Abstract

Dynamic manipulation enables efficient interaction tasks, such as throwing, which rely on finding one or more high-quality trajectories from the initial state to the goal state. While model-free learning methods have been used to acquire efficient robot manipulation configurations, traditional planning algorithms often struggle with multi-task specifications, high-dimensional, and multi-modal trajectory data. Prior generative model-based approaches, have made significant progress in the field of motion planning. Diffusion models, as an emerging generative model, have been widely applied to planning tasks in various environments and have gained attention for their ability in encoding multidimensional and multimodal trajectories. Here we propose our method that combines the diffusion model and model-free throwing methods. Specifically, we use a backward reachable tube to search for throwing configurations, and sample from posterior trajectory distribution conditioned on the throwing configurations. Several trajectory optimization methods are used to ensure the generation of effective throwing trajectories. Experimental results show that our method is effective in generating feasible, smooth, and collision-free throwing trajectories in both simulated and real-world tasks. Additionally, different trajectories are provided to enhance the multimodality of the throwing task.