Abstract-- Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks by integrating task planning with motion planning; the task planner proposes a sequence of actions towards a goal state and the motion planner verifies whether this action sequence is geometrically feasible for the robot. We aim to equip a robot with the ability to solve complex We propose a probabilistically-complete, plan-first TAMP long-horizon tasks that require a combination of symbolic algorithm that is significantly faster than PDDLStream and geometric reasoning. This speedup occurs by using a direct stream is an approach for solving such tasks. TAMP methods often planning algorithm to create stream objects after task use task planning to produce a sequence of symbolic planning rather than before to avoid the computational cost actions, i.e. a task plan, in addition to using sampling-based of task planning with many unnecessary stream objects. We motion planning to ensure the task plan is geometrically validate our method on three TAMP domains (Figure 1), each feasible.

Chemists need to perform many laborious and time-consuming experiments in the lab to discover and understand the properties of new materials. To support and accelerate this process, we propose a robot framework for manipulation that autonomously performs chemistry experiments. Our framework receives high-level abstract descriptions of chemistry experiments, perceives the lab workspace, and autonomously plans multi-step actions and motions. The robot interacts with a wide range of lab equipment and executes the generated plans. A key component of our method is constrained task and motion planning using PDDLStream solvers. Preventing collisions and spillage is done by introducing a constrained motion planner. Our planning framework can conduct different experiments employing implemented actions and lab tools. We demonstrate the utility of our framework on pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization.

This paper presents a novel algorithm for robot task and motion planning (TAMP) problems by utilizing a reachability tree. While tree-based algorithms are known for their speed and simplicity in motion planning (MP), they are not well-suited for TAMP problems that involve both abstracted and geometrical state variables. To address this challenge, we propose a hierarchical sampling strategy, which first generates an abstracted task plan using Monte Carlo tree search (MCTS) and then fills in the details with a geometrically feasible motion trajectory. Moreover, we show that the performance of the proposed method can be significantly enhanced by selecting an appropriate reward for MCTS and by using a pre-generated goal state that is guaranteed to be geometrically feasible. A comparative study using TAMP benchmark problems demonstrates the effectiveness of the proposed approach.

In the previous post, we introduced task planning in robotics. This field broadly involves a set of planning techniques that are domain-independent: That is, we can design a domain which describes the world at some (typically high) level of abstraction, using some modeling language like PDDL. However, the planning algorithms themselves can be applied to any domain that can be modeled in that language, and critically, to solve any valid problem specification within that domain. The key takeaway of the last post was that task planning is ultimately search. These search problems are often challenging and grow exponentially with the size of the problem, so it is no surprise that task planning is often symbolic: There are relatively few possible actions to choose from, with a relatively small set of finite parameters.