ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting
Kushal Kedia, Prithwish Dan, Atiksh Bhardwaj, Sanjiban Choudhury

TL;DR
ManiCast introduces a cost-aware human forecasting framework that improves real-time human-robot collaboration by focusing on how human motion impacts robot planning, demonstrated through various real-world tasks.
Contribution
This work presents ManiCast, a novel cost-aware forecasting approach integrated with model predictive control for improved collaborative manipulation.
Findings
Effective in real-time collaborative tasks
Outperforms heuristic baselines in accuracy
Enables fluid human-robot interactions
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · AI-based Problem Solving and Planning · Reinforcement Learning in Robotics
