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.

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.

Cooperative dynamic object manipulation extends the manipulation capabilities of human-robot dyads. This paper investigates cooperative swinging of rigid objects with the goal of reaching a desired level of energy, i.e. a desired object height. A human-human pilot study indicates that the arm-object-arm system can be approximated by a simple pendulum with two-sided unidirectional pulsed torque actuation. Based on the results of the human-human experiments, a robotic leader and follower controller are synthesized. Multi-body simulations based on human-like parameters successfully replicate the characteristics observed in the human-human experiments.