TY - GEN
T1 - Learning interaction for collaborative tasks with probabilistic movement primitives
AU - Maeda, Guilherme
AU - Ewerton, Marco
AU - Lioutikov, Rudolf
AU - Ben Amor, Heni
AU - Peters, Jan
AU - Neumann, Gerhard
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/2/12
Y1 - 2015/2/12
N2 - This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of imitation learning and its application to human-robot interaction using the concept of Interaction Primitives (IPs). We introduce the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. We evaluate our method on experiments using a lightweight arm interacting with a human partner and also using motion capture trajectories of two humans assembling a box. The advantages of ProMPs in relation to the original formulation for interaction are exposed and compared.
AB - This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of imitation learning and its application to human-robot interaction using the concept of Interaction Primitives (IPs). We introduce the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. We evaluate our method on experiments using a lightweight arm interacting with a human partner and also using motion capture trajectories of two humans assembling a box. The advantages of ProMPs in relation to the original formulation for interaction are exposed and compared.
UR - http://www.scopus.com/inward/record.url?scp=84945190639&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945190639&partnerID=8YFLogxK
U2 - 10.1109/HUMANOIDS.2014.7041413
DO - 10.1109/HUMANOIDS.2014.7041413
M3 - Conference contribution
AN - SCOPUS:84945190639
T3 - IEEE-RAS International Conference on Humanoid Robots
SP - 527
EP - 534
BT - 2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
PB - IEEE Computer Society
T2 - 2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
Y2 - 18 November 2014 through 20 November 2014
ER -