MSc Thesis Presentation - Nicholas Ioannidis

Name: Nicholas Ioannidis

Date: April 9, 2025 (Wed)

Time: 11:00 am

Location: ICCS 238

Supervisor: Prof. Michiel van de Panne

Title: Viability Estimation for Diffusion-Based Planning

Abstract:

Diffusion models can be used as a motion planner by sampling from a distribution of possible futures. However, the samples may not satisfy hard constraints that exist only implicitly in the training data, e.g., avoiding falls or not colliding with a wall. We propose learned viability filters that efficiently predict the future success of any given plan, i.e., diffusion sample, and thereby enforce an implicit future-success constraint. This serves to identify samples from the output of a diffusion-based motion planner with respect to implicit constraints and takes the general form of a learned Q-function.

This allows for efficient online planning with diffusion models, including for situations where the diffusion model and viability filter have asymmetric access to environment observations.

Multiple viability filters can also be composed together so that they are each taken into consideration. We demonstrate the approach on detailed footstep planning for challenging 3D human locomotion tasks, showing the effectiveness of viability filters in performing online planning and control for box-climbing, step-over walls, and obstacle avoidance. We further show that using viability filters is significantly faster than guidance-based diffusion prediction.