Robotic tasks often require generation of motions that satisfy multiple motion constraints , that may live on different parts of a robot's body . In this paper, we address the challenge of learning motion policies to generate motions for execution of such tasks. Additionally, to encode multiple motion constraints and their synergies, we enforce structure in our motion policy . Specifically, the structure results from decomposing a motion policy into multiple subtask policies , whereby each subtask policy dictates a particular subtask behavior. By learning the subtask policies together in an end-to-end fashion, our formulation not only learns coordination between subtask behaviors, but also learns how to trade them off against default behaviors that may exist . Furthermore, due to our choice of parameterization for the constituting subtask policies, our overall structured motion policy is guaranteed to generate stablemotions. To corroborate our theory, we also present qualitative and quantitative evaluations on multiple robotic tasks .
Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot . In this paper, we propose to solve multi-task problems through learning structured policies from human demonstrations. Our structured policy is inspired by RMPflow, a framework for combining subtask policies on different spaces. The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned. We derive an end-to-end learning objective function that is suitable for the multi-task problem, emphasizing the deviation of motions on task spaces . Furthermore, the motion generated from the learned policy class is guaranteed to be stable. We validate the effectiveness of our proposed learning framework through qualitative and quantitative evaluations on three robotic tasks on a 7-DOF Rethink Sawyer robot .
R
Robotic tasks often require generation of motions that satisfy multiple motion constraints , that may live on different parts of a robot's body
Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot
R
address the challenge of learning motion policies to generate motions for execution of such tasks. Additionally, to encode multiple motion constraints and their synergies, we enforce structure in our motion policy . Specifically, the structure results from decomposing a motion policy into multiple subtask policies , whereby each subtask policy dictates a particular subtask behavior. By learning the subtask policies together in an
propose to solve multi-task problems through learning structured policies from human demonstrations. Our structured policy is inspired by RMPflow, a framework for combining subtask policies on different spaces. The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned. We derive an
R
fashion, our formulation not only learns coordination between subtask behaviors, but also learns how to trade them off against default behaviors that may exist
learning objective function that is suitable for the multi-task problem, emphasizing the deviation of motions on task spaces
R
due to our choice of parameterization for the constituting subtask policies, our overall structured motion policy
the motion generated from the learned policy class
R
generate stablemotions. To corroborate our theory, we also present
be stable. We validate the effectiveness of our proposed learning framework through
R
multiple robotic tasks
three robotic tasks on a 7-DOF Rethink Sawyer robot