Seamless human-robot manipulation in close proximity relies on accurate
forecasts of human motion. While there has been significant progress in
learning forecast models at scale, when applied to manipulation tasks, these
models accrue high errors at critical transition points leading to degradation
in downstream planning performance. Our key insight is that instead of
predicting the most likely human motion, it is sufficient to produce forecasts
that 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 forecasts
and feeds them to a model predictive control planner to execute collaborative
manipulation tasks. Our framework enables fluid, real-time interactions between
a human and a 7-DoF robot arm across a number of real-world tasks such as
reactive stirring, object handovers, and collaborative table setting. We
evaluate both the motion forecasts and the end-to-end forecaster-planner system
against a range of learned and heuristic baselines while additionally
contributing new datasets. We release our code and datasets at
this https URL

人 - 机器人操作中准确预测人类动作是关键，本研究提出 ManiCast 框架，学习成本感知的人类预测并传递给模型预测控制计划器，实现合作操作任务。