7 accepted papers
We propose a hierarchical task and motion planning (TAMP) for autonomous construction that manipulates deformable objects, such as terrain excavation. The TAMP is required to generate an efficient task plan to meet high-level construction goals at sites with environmental diversity while ensuring mo…
Reinforcement learning with domain randomization (DR) has been proposed as a promising approach for learning robust policies to environmental changes. However, for DR to work well in real-world environments, it is necessary to design appropriate DR distributions for model parameters. This paper prop…
Hierarchical algorithms have often been used to plan and execute complicated robotic sequential manipulation tasks, where an abstract planner searches for a skill sequence in an abstract space, and each skill generates actual motions on the basis of the planned skill sequences. To generate executabl…
We propose a new optimization-based task and motion planning (TAMP) with signal temporal logic (STL) specifications for robotic sequential manipulation such as pick-and-place tasks. Given a high-level task specification, the TAMP problem is to plan a trajectory that satisfies the specification. This…
We present a formulation for linear temporal logic (LTL)-based task planning of nonlinear dynamical systems. We consider pick-and-place task planning as a typical example of the planning task that can be modeled as a hybrid system that includes the states of robots and objects. LTL-based planning fo…
In this paper, we propose a method for automatically generating object handling actions based on simple action definitions. The need to replace workers by robots is increasing, and, in fact, many research projects on robots have worked with simple motion definitions. Many applications are for mobile…
We propose a method of subdividing robot tasks into new lower abstract tasks. The description of robot tasks in an abstract manner is effective for motion planning for complex tasks and teaching robot movements in various environments. However, a more efficient task description may be obtained by us…