Diffusion-based Planning with Learned Viability Filters 62

Physics-based characters need to plan their movements over-and-around obstacles. Diffusion models offer a possible solution, as they allow a motion planner to sample from a potentially diverse distribution of possible futures. However, they may also generate flawed plans because some samples may not satisfy hard constraints that exist only implicitly in the training data, e.g., guaranteeing balance or obstacle clearance. We propose learned viability filters that can efficiently predict the future success of a given plan, i.e., diffusion sample, and thereby enforce an implicit future-success constraint. Multiple viability filters can also be composed together at run-time to take multiple potential constraints into consideration. We demonstrate the approach on detailed footstep planning for 3D human locomotion tasks, showing the effectiveness of the viability filters in performing online planning for box-climbing, step-over walls, and obstacle avoidance. We compare to a number of alternative planning methods including reinforcement learning and return-conditioned diffusion, and further show that using viability filters is significantly faster than guidance-based diffusion prediction.