Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
This addresses a key challenge in robotics for enabling more reliable and flexible planning in complex environments, though it is an incremental advancement building on existing diffusion techniques.
The paper tackles the problem of long-horizon robotic tasks where existing diffusion models struggle with complex decision-making and mode confusion, and proposes a hybrid diffusion method that simultaneously generates symbolic plans and continuous trajectories, resulting in dramatic performance improvements over baselines.
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy this, we propose to augment continuous trajectory generation by simultaneously generating a high-level symbolic plan. We show that this requires a novel mix of discrete variable diffusion and continuous diffusion, which dramatically outperforms the baselines. In addition, we illustrate how this hybrid diffusion process enables flexible trajectory synthesis, allowing us to condition synthesized actions on partial and complete symbolic conditions.