We propose a new policy class, Composable Interaction Primitives (CIPs), specialized for
learning sustained-contact manipulation skills like opening a drawer, pulling a lever, turning …

Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to
different situations. Task-parameterized learning improves the generalization of motion …

Learning to interact with the environment not only empowers the agent with manipulation
capability but also generates information to facilitate building of action understanding and …

Learning sensorimotor trajectories through flexible neural representations is fundamental for
robots as it facilitates the building of motor skills as well as equip** them with the ability to …

We propose a new policy class, Composable Interaction Primitives (CIPs), specialized for
learning sustained-contact manipulation skills like opening a drawer, pulling a lever, turning …

Learning from demonstration (LfD) with behavior cloning is attractive for its simplicity;
however, compounding errors in long and complex skills can be a hindrance. Considering a …

One of the most important challenges in robotics is producing accurate trajectories and
controlling their dynamic parameters so that the robots can perform different tasks. The …