To economically deploy robotic manipulators the programming and execution of robot motions must be swift. To this end, we propose a novel, constraint-based method to intuitively specify sequential manipulation tasks and to compute time-optimal robot motions for such a task specification. Our approach follows the ideas of constraint-based task specification by aiming for a minimal and object-centric task description that is largely independent of the underlying robot kinematics. We transform this task description into a non-linear optimization problem. By solving this problem we obtain a (locally) time-optimal robot motion, not just for a single motion, but for an entire manipulation sequence. We demonstrate the capabilities of our approach in a series of experiments involving five distinct robot models, including a highly redundant mobile manipulator.

In this paper we propose a new model for sequential manipulation tasks that also considers robot dynamics and time-variant environments. From this model we automatically derive constraint-based controllers and use them as steering functions in a kinodynamic manipulation planner. The resulting plan is not a trajectory but a sequence of controllers that react online to disturbances. We validated our approach in simulation and on a real robot. In the experiments our approach plans and executes dual-robot manipulation tasks with online collision avoidance and reactions to estimates of object poses.

This paper introduces a Bayesian state estimator for contact-rich manipulation tasks with application in non-prehensile manipulation, industrial assembly or in-hand localization. The core idea of our approach is to explicitly model both the contact dynamics and a torque-based robot controller as part of the underlying system model. Our approach is capable of estimating the state of movable objects for various robot kinematics and geometries of robots and objects. This includes complex scenarios with multiple robots, multiple objects and articulated objects. We have validated our approach in simulation and on a physical robot. The experiments show that multimodal distributions of six degrees of freedom object poses can be accurately tracked in real-time in a complex manipulation scenario.

When robots perform manipulation tasks, they need to determine their own movement, as well as how to make and break contact with objects in their environment. Reasoning about the motions of robots and objects simultaneously leads to a constrained planning problem in a high-dimensional state-space. Additionally, when environments change dynamically motions must be computed in real-time. To this end, we propose a feedback planner for manipulation. We model manipulation as constrained motion and use this model to automatically derive a set of constraint-based controllers. These controllers are used in a switching-control scheme, where the active controller is chosen by a reinforcement learning agent. Our approach is capable of addressing tasks with second-order dynamics, closed kinematic chains, and time-variant environments. We validated our approach in simulation and on a real, dual-arm robot. Extensive simulation of three distinct robots and tasks show a significant increase in robustness compared to a previous approach.

In this paper, we present an approach to generate robot motions for robust parts assembly. The computation of motions for parts assembly usually requires an exact model of all relevant objects. Generating detailed object models, including friction and dynamics, is often complex and time-consuming, especially in the context of elastic parts. In addition, executing motions on real hardware will usually introduce further uncertainty. For this reason, we propose an approach that is inherently robust against model parameter uncertainties and unknown characteristics of elastic parts. Our planner explicitly takes into account the internal states of articulated objects, as well as uncertain model parameters, by constructing a search tree in the belief-parameter-space. It yields successful assembly motions from coarse object models and thus eliminates the need for detailed parameter tuning. We evaluated our approach with respect to four assembly tasks. Extensive simulations show that our planner significantly increases the success-rate compared to previous approaches. Numerous experiments on a real robot confirm the simulated results.

Sensor guided, automated systems require the composition of various sensors and data processing algorithms to obtain relevant information for performing their task. Many applications have additional requirements such as a certain accuracy, which has to be achieved despite sensor noise and calibration errors. In this paper we model the configuration of perception systems as a planning problem over probabilistic graphical models. We work on a subset of the full configuration space of perceptions systems, specifically the used sensors, data processing algorithms and view poses. Based on a semantic description of the goal, available sensors and data processing algorithms, our system plans perception steps and sensor data fusion autonomously. The planner operates by constructing a factor graph until the accuracy requirements of tasks are fulfilled or unobtainable with the available action set. We validate our approach in an industrial assembly scenario.

In automated manufacturing, robots must reliably assemble parts of various geometries and low tolerances. Ideally, they plan the required motions autonomously. This poses a substantial challenge due to high-dimensional state spaces and non-linear contact-dynamics. Furthermore, object poses and model parameters, such as friction, are not exactly known and a source of uncertainty. The method proposed in this paper models the task of parts assembly as a belief space planning problem over an underlying impedance-controlled, compliant system. To solve this planning problem we introduce an asymptotically optimal belief space planner by extending an optimal, randomized, kinodynamic motion planner to nondeterministic domains. Under an expansiveness assumption we establish probabilistic completeness and asymptotic optimality. We validate our approach in thorough, simulated and realworld experiments of multiple assembly tasks. The experiments demonstrate our planner's ability to reliably assemble objects, solely based on CAD models as input.

When robots perform manipulation tasks, they need to determine their own movement, as well as how to grasp and release an object. Reasoning about the motion of the robot and the object simultaneously leads to a multi-modal planning problem in a high-dimensional configuration space. In this paper we propose an asymptotically optimal manipulation planner. Our approach extends optimal sampling-based roadmap planners to efficiently explore the configuration space of the robot and the object. We prove probabilistic completeness and global, asymptotic optimality. Extensive simulations of a typical pick-and-place scenario show that our approach significantly outperforms a (nonoptimal) state-of-the-art approach. We implemented our planner on a real manipulator and were able to compute high quality solutions in less than a second.