Abstract — This paper combines grasp analysis and manipulation planning techniques to perform fast grasp planning in complex scenes. In much previous work on grasping, the object being grasped is assumed to be the only object in the environment. Hence the grasp quality metrics and grasping strategies developed do not perform well when the object is close to obstacles and many good grasps are infeasible. We introduce a framework for finding valid grasps in cluttered environments that combines a grasp quality metric for the object with information about the local environment around the object and information about the robot’s kinematics. We encode these factors in a grasp-scoring function which we use to rank a precomputed set of grasps in terms of their appropriateness for a given scene. We show that this ranking is essential for efficient grasp selection and present experiments in simulation and on the HRP2 robot. I.

Abstract. We consider multi-robot systems where robots need to cooperate tightly by sharing functionalities with each other. There are methods for automatically configuring a multi-robot system for tight cooperation, but they only produce a single configuration. In this paper, we show how methods for automatic configuration can be integrated with methods for task planning in order to produce a complete plan were each step is a configuration. We also consider the issues of monitoring and replanning in this context, and we demonstrate our approach on a real multi-robot system, the PEIS-Ecology. 1

Abstract. The newly established cluster of excellence COTESYS 1 investigates the realization of cognitive capabilities such as perception, learning, reasoning, planning, and execution for technical systems including humanoid robots, flexible manufacturing systems, and autonomous vehicles. In this paper we describe cognitive technical systems using a sensor-equipped kitchen with a robotic assistant as an example. We will particularly consider the role of Artificial Intelligence in the research enterprise. Key research foci of Artificial Intelligence research in COTESYS include (◦) symbolic representations grounded in perception and action, (◦) first-order probabilistic representations of actions, objects, and situations, (◦) reasoning about objects and situations in the context of everyday manipulation tasks, and (◦) the representation and revision of robot plans for everyday activity. 1

Every planning problem in robotics involves constraints. Whether the robot must avoid collision or joint limits, there are always states that are not permissible. Some constraints are straightforward to satisfy while others can be so stringent that feasible states are very difficult to find. What makes planning with constraints challenging is that, for many constraints, it is impossible or impractical to provide the planning algorithm with the allowed states explicitly; it must discover these states as it plans. The goal of this thesis is to develop a framework for representing and exploring feasible states in the context of manipulation planning. Planning for manipulation gives rise to a rich variety of tasks that include constraints on collisionavoidance, torque, balance, closed-chain kinematics, and end-effector pose. While many researchers have developed representations and strategies to plan with a specific constraint, the goal of this thesis is to develop a broad representation of constraints on a robot’s configuration and identify general strategies to manage these constraints during the planning process. Some of the most important constraints in manipulation planning are functions of the pose of the manipulator’s end-effector, so we devote a large part of this thesis to end-effector placement for grasping and transport tasks. We present an efficient approach to generating paths that uses Task Space Regions (TSRs) to specify manipulation

Abstract. We describe a proximity query algorithm for the exact minimum distance computation between arbitrarily shaped objects. Special characteristics of the Gilbert-Johnson-Keerthi (GJK) algorithm are employed in various stages of the algorithm. In the first stage, they are used to search for sub-mesh pairs whose convex hulls do not intersect. In the case of an intersection, they guide a recursive decomposition. Finally, they are used to derive lower and upper distance bounds in non-intersecting cases. These bounds are utilized in a spatial subdivision scheme to achieve a twofold culling of the domain. The algorithm does not depend on spatial or temporal coherence and is, thus, specifically suited to be applied to deformable objects. Furthermore, we describe its embedding into the geometrical part of a mobile manipulation planning system. Experiments show its usability in dynamic scenarios with deformable objects as well as in complex manipulation planning scenarios. 1

Robots reason about abstract actions, such as go to position ‘l’, in order to decide what to do or to generate plans for their intended course of action. The use of abstract actions enables robots to employ small action libraries, which reduces the search space for decision making. When executing the actions, however, the robot must tailor the abstract actions to the specific task and situation context at hand. In this article we propose a novel robot action execution system that learns success and performance models for possible specializations of abstract actions. At execution time, the robot uses these models to optimize the execution of abstract actions to the respective task contexts. The robot can so use abstract actions for efficient reasoning, without compromising the performance of action execution. We show the impact of our action execution model in three robotic domains and on two kinds of action execution problems: (1) the instantiation of free action parameters to optimize the expected performance of action sequences; (2) the automatic introduction of additional subgoals to make action sequences more reliable. 1.

Every planning problem in robotics involves constraints. Whether the robot must avoid collision or joint limits, there are always states that are not permissible. Some constraints are straightforward to satisfy while others can be so stringent that feasible states are very difficult to find. What makes planning with constraints challenging is that, for many constraints, it is impossible or impractical to provide the planning algorithm with the allowed states explicitly; it must discover these states as it plans. The goal of this thesis is to develop a framework for representing and exploring feasible states in the context of manipulation planning. Planning for manipulation gives rise to a rich variety of tasks that include constraints on collisionavoidance, torque, balance, closed-chain kinematics, and end-effector pose. While many researchers have developed representations and strategies to plan with a specific constraint, the goal of this thesis is to develop a broad representation of constraints on a robot’s configuration and identify general strategies to manage these constraints during the planning process. Some of the most important constraints in manipulation planning are functions of the pose of the manipulator’s end-effector, so we

We propose a representation and a planning algorithm able to deal with problems integrating task planning as well as motion and manipulation planning knowledge involving several robots and objects. Robot plans often include actions where the robot has to place itself in some position in order to perform some other action or to “modify” the configuration of its environment by displacing objects. Our approach aims at establishing a bridge between task planning and manipulation planning that allows a rigorous treatment of geometric preconditions and effects of robot actions in realistic environments. We show how links can be established between a symbolic description and its geometric counterpart and how they can be used in an integrated planning process that is able to deal with intricate symbolic and geometric constraints. Finally, we describe the main features of an implemented planner and discuss several examples of its use. KEY WORDS—planning formalism, task planning, motion planning, manipulation planning. 1.