TY - GEN
T1 - Interactive online learning of the kinematic workspace of a humanoid robot
AU - Jamone, Lorenzo
AU - Natale, Lorenzo
AU - Sandini, Giulio
AU - Takanishi, Atsuo
PY - 2012/12/1
Y1 - 2012/12/1
N2 - We describe an interactive learning strategy that enables a humanoid robot to build a representation of its workspace: we call it a Reachable Space Map. The robot learns this map autonomously and online during the execution of goal-directed reaching movements; reaching control is based on kinematic models that are learned online as well. The map can be used to estimate the reachability of a fixated object and to plan preparatory movements (e.g. bending or rotating the waist) that improve the effectiveness of the subsequent reaching action. Three main concepts make our solution innovative with respect to previous works: the use of a gaze-centered motor representation to describe the robot workspace, the primary role of action in building and representing knowledge (i.e. interactive learning), the realization of autonomous online learning. We evaluate our strategy by learning the workspace of a simulated humanoid robot and we show how this knowledge can be exploited to plan and execute complex actions, like whole-body bimanual reaching.
AB - We describe an interactive learning strategy that enables a humanoid robot to build a representation of its workspace: we call it a Reachable Space Map. The robot learns this map autonomously and online during the execution of goal-directed reaching movements; reaching control is based on kinematic models that are learned online as well. The map can be used to estimate the reachability of a fixated object and to plan preparatory movements (e.g. bending or rotating the waist) that improve the effectiveness of the subsequent reaching action. Three main concepts make our solution innovative with respect to previous works: the use of a gaze-centered motor representation to describe the robot workspace, the primary role of action in building and representing knowledge (i.e. interactive learning), the realization of autonomous online learning. We evaluate our strategy by learning the workspace of a simulated humanoid robot and we show how this knowledge can be exploited to plan and execute complex actions, like whole-body bimanual reaching.
UR - http://www.scopus.com/inward/record.url?scp=84872311687&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2012.6385595
DO - 10.1109/IROS.2012.6385595
M3 - Conference contribution
AN - SCOPUS:84872311687
SN - 9781467317375
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2606
EP - 2612
BT - 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
T2 - 25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
Y2 - 7 October 2012 through 12 October 2012
ER -