Robotic applications often involve working in environments that are uncertain, dynamic, and partially observable. Recently, diffusion models have been proposed for learning trajectory prediction models trained from expert demonstrations, which can be used for planning in robot tasks. Such models have demonstrated a strong ability to overcome challenges such as multi-modal action distributions, high-dimensional output spaces, and training instability. It is crucial to quantify the uncertainty of these dynamics models when using them for planning. In this paper, we quantify the uncertainty of diffusion dynamics models using Conformal Prediction (CP).

Robotic applications often involve working in environments that are uncertain, dynamic, and partially observable. Recently, diffusion models have been proposed for learning trajectory prediction models trained from expert demonstrations, which can be used for planning in robot tasks. Such models have demonstrated a strong ability to overcome challenges such as multi-modal action distributions, high-dimensional output spaces, and training instability. It is crucial to quantify the uncertainty of these dynamics models when using them for planning. In this paper, we quantify the uncertainty of diffusion dynamics models using Conformal Prediction (CP).

Planning under uncertainty is a fundamental challenge in robotics. For multi-robot teams, the challenge is further exacerbated, since the planning problem can quickly become computationally intractable as the number of robots increase. In this paper, we propose a novel approach for planning under uncertainty using heterogeneous multi-robot teams. In particular, we leverage the notion of a dynamic topological graph and mixed-integer programming to generate multi-robot plans that deploy fast scout team members to reduce uncertainty about the environment. We test our approach in a number of representative scenarios where the robot team must move through an environment while minimizing detection in the presence of uncertain observer positions. We demonstrate that our approach is sufficiently computationally tractable for real-time re-planning in changing environments, can improve performance in the presence of imperfect information, and can be adjusted to accommodate different risk profiles.