Recognizing Intent in Collaborative Manipulation
Zhanibek Rysbek, Ki Hwan Oh, Milos Zefran

TL;DR
This paper presents a framework for recognizing human intent in collaborative manipulation using force signals, enabling robots to better interpret human actions and improve cooperation in physical tasks.
Contribution
It introduces a novel set of features and a classifier trained on human-human interaction data to accurately identify human intent during collaborative manipulation.
Findings
High overall accuracy in intent recognition
Robustness against partner action variations
Effective identification of interaction transitions
Abstract
Collaborative manipulation is inherently multimodal, with haptic communication playing a central role. When performed by humans, it involves back-and-forth force exchanges between the participants through which they resolve possible conflicts and determine their roles. Much of the existing work on collaborative human-robot manipulation assumes that the robot follows the human. But for a robot to match the performance of a human partner it needs to be able to take initiative and lead when appropriate. To achieve such human-like performance, the robot needs to have the ability to (1) determine the intent of the human, (2) clearly express its own intent, and (3) choose its actions so that the dyad reaches consensus. This work proposes a framework for recognizing human intent in collaborative manipulation tasks using force exchanges. Grounded in a dataset collected during a human study, we…
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