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Seamless human-robot manipulation in close proximity relies on accurateforecasts of human motion. While there has been significant progress inlearning forecast models at scale, when applied to manipulation tasks, thesemodels accrue high errors at critical transition points leading to degradationin downstream planning performance. Our key insight is that instead ofpredicting the most likely human motion, it is sufficient to produce forecaststhat capture how future human motion would affect the cost of a robot's plan.We present ManiCast, a novel framework that learns cost-aware human forecastsand feeds them to a model predictive control planner to execute collaborativemanipulation tasks. Our framework enables fluid, real-time interactions betweena human and a 7-DoF robot arm across a number of real-world tasks such asreactive stirring, object handovers, and collaborative table setting. Weevaluate both the motion forecasts and the end-to-end forecaster-planner systemagainst a range of learned and heuristic baselines while additionallycontributing new datasets. We release our code and datasets athttps://portal-cornell.github.io/manicast/.

ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting