Automating long-horizon tasks with a robotic arm has been a central research topic in robotics. Optimization-based action planning is an efficient approach for creating an action plan to complete a given task. Construction of a reliable planning method requires a design process of conditions, e.g., to avoid collision between objects. The design process, however, has two critical issues: 1) iterative trials–the design process is time-consuming due to the trial-and-error process of modifying conditions, and 2) manual redesign–it is difficult to cover all the necessary conditions manually. To tackle these issues, this paper proposes a future-predictive success-or-failure-classification method to obtain conditions automatically. The key idea behind the proposed method is an end-to-end approach for determining whether the action plan can complete a given task instead of manually redesigning the conditions. The proposed method uses a long-horizon future-prediction method to enable success-or-failure classification without the execution of an action plan. This paper also proposes a regularization term called transition consistency regularization to provide easy-to-predict feature distribution. The regularization term improves future prediction and classification performance. The effectiveness of our method is demonstrated through classification and robotic-manipulation experiments.

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 motion feasibility. The difficulty, however, is to manipulate deformable objects containing nonlinear dynamics with a target given by a continuous value as the task specification. Optimization-based TAMP with signal temporal logic specifications in robotics is promising because of its continuous task specification and formulation as a nonlinear programming problem. The key to its application to extensive, long-term planning at real construction sites is a computationally efficient and stable formulation. We introduce a new expression for deformable objects with a simple differentiable function and a system model that can represent mode transitions based on machine action on the objects. This allows TAMP to be formulated as simultaneously selecting an action for objects and planning the motion to execute it. Furthermore, a hierarchical method that gives appropriate initial values is combined to improve optimality for large-scale nonlinear problems. From the verification by numerical experiments, the proposed method can generate a plan that minimizes the time to meet the task goal, even when the area is expanded.

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 proposes Normalizing Flow-based Parameter Distribution Estimation (NFPDE), a new estimation method for DR distributions. NFPDE models the target distribution by a flow-based generative model using normalizing flow and estimates the target distribution based on an offline dataset collected a priori in the target environment. Through numerical experiments on the OpenAI gym environment, we show that NFPDE can estimate the target distribution more accurately and efficiently than the previous estimation methods. We also show that the estimated DR distributions can improve the robustness of trained policies.

We present a formulation for a linear temporal logic (LTL)-based task planning using the Koopman operator. The dynamics of nonlinear systems can be represented as linear systems by lifting them to a space of augmented states using the Koopman operator. On the other hand, the lifted linear system cannot capture the nonlinear effects of inputs, which appear in many robotic systems. Therefore, instead of a lifted linear system, we can consider representing control-affine bilinear systems. However, since the lifted bilinear systems are nonlinear, we need to solve nonlinear programming problems for trajectory optimization. This paper presents a methodology for the trajectory optimization problem of the lifted bilinear system. Using the mixed-integer convex approximation, we can solve the trajectory optimization problem of the lifted bilinear systems as a mixed-integer linear programming problem. This formulation allows us to solve LTL-based task planning problems for nonlinear systems. The effectiveness of the proposed method was confirmed by numerical simulations.

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 executable plans, the abstract planner should know the pre-/postconditions of each skill and appropriately choose skills so that the generated plan satisfies their pre-/postconditions. For such hierarchical planning, this paper presents a novel method for robot skill learning that learns not only a control policy but also the learned skill's pre-/postconditions to complete a given task. Our method combines an optimal control method and an active learning approach called level set estimation (LSE) to effectively collect training data for learning control policies and pre-/postconditions. Although there exists a LSE-based policy learning algorithm that identifies preconditions, its performance is limited to cases where the dimension of the search space for pre-/postconditions is low. The main contribution of this paper is the proposal of a new learning method that can handle tasks having a high-dimensional search space for pre-/postconditions. We demonstrate our proposed method in two robotic tasks. The results show that our method can more effectively learn a control policy and its pre-/postconditions compared with the existing LSE-based method.

We propose a fast optimization algorithm to find feasible solutions for a special class of switching structured problems. A type of continuous optimization problem that has switching structured constraints is called mathematical programs with switching constraints (MPSC). Relaxation methods are well known gradient descent-based approaches for solving MPSC. However, with the use of these conventional algorithms, we reveal that the sequence of solutions easily converges to an infeasible stationary point due to a special class of switching constraint whose feasible set is geometrically separated into mutually exclusive sets in variable space. We define this kind of switching constraint as the disjunctive allowable set constraint (DAS-constraint). To force the sequence of solutions to escape from such infeasible stationary points, we construct a new algorithm that introduces random sampling if convergence to an infeasible point is detected. Furthermore, to reduce the computation cost due to random sampling, we randomize only specific variables that are relevant to DAS-constraints. Numerical experiments show that feasible solutions can be found by using our proposed algorithm, even for large-scale optimization problems where no solutions are found within a practical time limit when using a conventional algorithm.

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 is, however, a challenging problem due to the difficulty of combining continuous motion planning and discrete task specifications. The optimization-based TAMP with temporal logic specifications is a promising method, but existing works use mixed integer problems (MIP) and do not scale well. To address this issue, in our approach, a new hybrid system model without discrete variables is introduced and combined with smooth approximation methods for STL. This allows the TAMP to be formulated as a nonlinear programming problem whose computational cost is significantly less than that of MIP. Furthermore, it is also possible to deal with nonlinear dynamics and geometric constraints represented by nonlinear functions. The effectiveness of the proposed method is demonstrated with both numerical experiments and a real robot.

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 for hybrid systems is solved as a mixed-integer problem (MIP), especially a mixed-integer linear programming problem (MILP). Due to the formulation by the MILP, we could only deal with linear systems and linear constraints. In our proposed method, we apply a convex approximation to systems that have bilinear terms and quadratic terms in their dynamics. And we incorporate nonlinear systems into existing LTL-based planning as an MILP. We demonstrate the effectiveness through numerical simulations of a simple robot arm system and drone system.

We present new models of optimization-based task and motion planning (TAMP) for robotic pick-and-place (P&P), which plan action sequences and motion trajectory with low computational costs. We improved an existing state-of-the-art TAMP model integrated with the collision avoidance, which is formulated as a mixed-integer linear programing (MILP) problem. To enable the MILP solver to search for solutions efficiently, we introduced two approaches leveraging features of collision avoidance in robotic P&P. The first approach reduces number of binary variables, which are related to the collision avoidance of delivery objects, by reformulating them as continuous variables with additional hard constraints. These hard constraints maintain consistency by conditionally propagating binary values, which are related to the carry action state and collision avoidance of robots, to the reformulated continuous variables. The second approach is more aware of the branch-and-bound method, which is the fundamental algorithm of modern MILP solvers. This approach guides the MILP solver to find integer solutions with shallower branching by adding a soft constraint, which softly restricts a robot’s routes around delivery objects. We demonstrate the effectiveness of the proposed approaches with a modern MILP solver.

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 robots such as drones, however, and if such methods are applied directly to object handling, like a pick and place operation, it is necessary for humans to give detailed instructions. Hence, our contribution is to propose a model that simulates the real world with an augmented hybrid system that includes the states of objects. Then, it becomes possible to automatically generate robot motions with simple motion definitions and calculate them within a reasonable time. We demonstrate through computer simulation with a dual-arm robot that robot motions can be generated by simple definitions even if the environment changes to a certain degree.

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 using a lower abstraction according to the work environment. We argue that a higher abstract task can be expressed as a new lower abstract subtasks by applying Grid-based Signal Temporal Inference (Grid-TLI). We show that a new task can be completed using the Signal Temporal Logic formula for each cluster. We demonstrated the efficiency of our method through computer simulations using a 2-D security robot task.