LHMP 2021, Part 10: Invited talk by Nick Rhinehart (UC Berkeley)
3rd Workshop on Long-term Human Motion Prediction (LHMP)
Part 10: Invited talk by Nick Rhinehart (UC Berkeley)
I will describe our recent efforts on enabling learning-based forecasting methods to help agents make complex decisions. First, I will describe our method for tractable contingency planning by learning predictive behavioral models. Then, I will describe a method that offers a compelling alternative to the standard motion forecasting pipeline by inverting it: the first step in this method is to "forecast everything" (in our case, LiDAR videos). I will conclude with thoughts on scaling these methods towards a more general system for making complex decisions by learning to forecast everything.
IEEE International Conference on Robotics and Automation (ICRA),
May 31, 2021
https://motionpredictionicra2021.gith...