We present a skill for the perception of three-dimensional kinematic structures of rigid articulated bodies with revolute and prismatic joints. The ability to acquire such models autonomously is required for general manipulation in unstructured environments. Experiments on a mobile manipulation platform with realworld objects under varying lighting conditions demonstrate the robustness of the proposed method. This robustness is achieved by integrating perception and manipulation capabilities: the manipulator interacts with the environment to move an unknown object, thereby creating a perceptual signal that reveals the kinematic properties of the object. For good performance, the perceptual skill requires the presence of trackable visual features in the scene.

A motion planning algorithm computes the motion of a robot by computing a path through its configuration space. To improve the runtime of motion planning algorithms, we propose to nest robots in each other, creating a nested quotientspace decomposition of the configuration space. Based on this decomposition we define a new roadmap-based motion planning algorithm called the Quotient-space roadMap Planner (QMP). The algorithm starts growing a graph on the lowest dimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open motion planning library (OMPL).

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Motion planning problems often have many local minima. Those minima are important to visualize to let a user guide, prevent or predict motions. Towards this goal, we develop the motion planning explorer, an algorithm to let users interactively explore a tree of local-minima. Following ideas from Morse theory, we define local minima as paths invariant under minimization of a cost functional. The localminima are grouped into a local-minima tree using lowerdimensional projections specified by a user. The user can then interactively explore the local-minima tree, thereby visualizing the problem structure and guide or prevent motions. We show the motion planning explorer to faithfully capture local minima in four realistic scenarios, both for holonomic and certain nonholonomic robots.

To solve complex whole-body motion planning problems in near real-time, we think it essentials to precompute as much information as possible, including our intended movements and how they affect the geometrical reasoning process. In this paper, we focus on the precomputation of the feasibility of contact transitions in the context of discrete contact planning. Our contribution is twofold: First, we introduce the contact transition and object (CTO) space, a joint space of contact states and geometrical information. Second, we develop an algorithm to precompute the decision boundary between feasible and nonfeasible spaces in the CTO space. This boundary is used for online-planning in classical contact-transition spaces to quickly prune the number of possible future states. By using a classical planning setup of A* together with a l2-norm heuristic, we demonstrate how the prior knowledge about object geometries can achieve near real-time performance in highly-cluttered environments, thereby not only outperforming the state-ofthe-art algorithm, but also having a significantly lower model sparsity.