We present a data-driven, bottom-up, deep learning approach to robotic grasping of unknown objects using Deep Convolutional Neural Networks (DCNNs). The approach uses depth images of the scene as its sole input for synthesis of a single-grasp solution during execution, adequately portraying the robot's visual perception during exploration of a scene. The training input consists of precomputed high-quality grasps, generated by analytical grasp planners, accompanied with rendered depth images of the training objects. In contrast to previous work on applying deep learning techniques to robotic grasping, our approach is able to handle full end-effector poses and therefore approach directions other than the view direction of the camera. Furthermore, the approach is not limited to a certain grasping setup (e. g. parallel jaw gripper) by design. We evaluate the method regarding its force-closure performance in simulation using the KIT and YCB object model datasets as well as a big data grasping database. We demonstrate the performance of our approach in qualitative grasping experiments on the humanoid robot ARMAR-III.

Robotic coverage path planning describes the problem of determining a configuration space trajectory for successively covering a specified workspace target area with the robot's end-effector. Performing coverage path planning for mobile robots further requires solving the problem of robot placement, i.e. determining of suitable robot base positions to perform the task. Finding an optimal solution is hard as both problems cannot be solved independently. Combined robot placement and coverage planning is particularly interesting if repositioning of the robot is costly or if simultaneous repositioning and end-effector motion is not desired. In this paper, we present a general approach for combined robot placement and coverage path planning that takes constraints like collision avoidance and static stability into account. In contrast to related approaches, we focus on mobile manipulation tasks that require a fixed placement for executing coverage trajectory segments. The approach is evaluated in two scenarios that exemplify the broad range of possible applications: The coverage of a building facade using a robotic manlift and the coverage of an industrial conveyer belt for maintenance tasks using the humanoid robot ARMAR-III.

We address the question of how resource-aware concepts can be utilized in motion planning algorithms. Resource-awareness facilitate better resource allocation on global system level, e.g. when a humanoid robot needs to distribute and schedule a wide variety of concurrent algorithms. We present a motion planning approach that employs self-monitoring concepts in order to identify the difficulty of the planning problem. Resources are requested dynamically and adapted based on problem difficulty and current planning progress. We show how dynamic adaptation of resource allocation on algorithmic level can reduce the system workload as compared to static resource allocation while meeting Quality of Service (QoS) measures such as average workload or efficiency. We evaluate our approach both in several synthetic setups with varying difficulty and with the humanoid robot ARMAR-4.

Object shape information is essential for robot manipulation tasks, in particular for grasp planning and collision-free motion planning. But in general a complete object model is not available, in particular when dealing with unknown objects. We propose a method for completing shapes that are only partially known, which is a common situation when a robot perceives a new object only from one direction. Our approach is based on the assumption that most objects used in service robotic setups have symmetries. We determine and rate symmetry plane candidates to estimate the hidden parts of the object. By finding possible supporting planes based on its immediate neighborhood, the search space for symmetry planes is restricted, and the bottom part of the object is added. Gaps along the sides in the direction of the view axis are closed by linear interpolation. We evaluate our approach with real-world experiments using the YCB object and model set [1].

Large-scale human motion databases are key for research questions ranging from human motion analysis and synthesis, biomechanics of human motion, data-driven learning of motion primitives, and rehabilitation robotics to the design of humanoid robots and wearable robots such as exoskeletons. In this paper we present a large-scale database of whole-body human motion with methods and tools, which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject-specific motions to robots with different embodiments. To this end, captured subject-specific motion is normalized regarding the subject's height and weight by using a reference kinematics and dynamics model of the human body, the master motor map (MMM). In contrast with previous approaches and human motion databases, the motion data in our database consider not only the motions of the human subject but the position and motion of objects with which the subject is interacting as well. In addition to the description of the MMM reference model, we present procedures and techniques for the systematic recording, labeling, and organization of human motion capture data, object motions as well as the subject–object relations. To allow efficient search for certain motion types in the database, motion recordings are manually annotated with motion description tags organized in a tree structure. We demonstrate the transfer of human motion to humanoid robots and provide several examples of motion analysis using the database.

Having a representation of the capabilities of a robot is helpful when online queries, such as solving the inverse kinematics (IK) problem for grasping tasks, must be processed efficiently in the real world. When workspace representations, e.g. the reachability of an arm, are considered, additional quality information such as manipulability or self-distance can be employed to enrich the spatial data. In this work we present an approach of inverting such precomputed reachability representations in order to generate suitable robot base positions for grasping. Compared to existing works, our approach is able to generate a distribution in SE(2), the cross-space consisting of 2D position and 1D orientation, that describes potential robot base poses together with a quality index. We show how this distribution can be queried quickly in order to find oriented base poses from which a target grasping pose is reachable without collisions. The approach is evaluated in simulation using the humanoid robot ARMAR-III [1] and an extension is presented that allows to find suitable base poses for trajectory execution.

Planning motion for humanoid robots requires obeying simultaneous constraints on balance, collision-avoidance, and end-effector pose, among others. Several algorithms are able to generate configurations that satisfy these constraints given a good initial guess, i.e. a configuration which is already close to satisfying the constraints. However, when selecting goals for a planner a close initial guess is rarely available. Methods that attempt to satisfy all constraints through direct projection from a distant initial guess often fail due to opposing gradients for the various constraints, joint-limits, or singularities. We approach the problem of generating a constrained goal by searching for a configuration in the intersection of all constraint manifolds in configuration space (C-space). Starting with an initial guess, our algorithm, Constellation, builds a graph in C-space whose nodes are configurations that satisfy one or more constraints and whose cycles determine where the algorithm explores next. We compare the performance of our approach to direct projection and a previously-proposed cyclic projection method on reaching tasks for a humanoid robot with 33 DOF. We find that Constellation performs the best in terms of the number of solved queries across a wide range of problem difficulty. However, this success comes at higher computational cost.

In this paper parameter-free concepts for exact motion planning are investigated. With the proposed RDT+approach the collision detection parameters of a Rapidly exploring Dense Tree (RDT) are automatically adjusted until an exact solution can be found. For efficient planning discrete collision detection routines are used within the RDT planner and by verifying the results with exact collision detection methods, the RDT+ concept allows to compute motions that are guaranteed collision-free. We show the probabilistic completeness of the proposed planner and present an extension for handling narrow passages. The algorithms are evaluated in different experiments, including narrow passages and high-dimensional planning problems, that are solved in simulation and on the humanoid robot ARMAR-III.

In this work, we present a new software environment for the comparative evaluation of algorithms for grasping and dexterous manipulation. The key aspect in its development is to provide a tool that allows the reproduction of well-defined experiments in real-life scenarios in every laboratory and, hence, benchmarks that pave the way for objective comparison and competition in the field of grasping. In order to achieve this, experiments are performed on a sound open-source software platform with an extendable structure in order to be able to include a wider range of benchmarks defined by robotics researchers. The environment is integrated into the OpenGRASP toolkit that is built upon the OpenRAVE project and includes grasp-specific extensions and a tool for the creation/integration of new robot models. Currently, benchmarks for grasp and motion planningare included as case studies, as well as a library of domestic everyday objects models, and a real-life scenario that features a humanoid robot acting in a kitchen.

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRT-based algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque.We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

In this paper, we present efficient solutions for planning motions of dual-arm manipulation and re-grasping tasks. Motion planning for such tasks on humanoid robots with a high number of degrees of freedom (DoF) requires computationally efficient approaches to determine the robot's full joint configuration at a given grasping position, i.e. solving the Inverse Kinematics (IK) problem for one or both hands of the robot. In this context, we investigate solving the inverse kinematics problem and motion planning for dual-arm manipulation and re-grasping tasks by combining a gradient-descent approach in the robot's pre-computed reachability space with random sampling of free parameters. This strategy provides feasible IK solutions at a low computation cost without resorting to iterative methods which could be trapped by joint-limits. We apply this strategy to dual-arm motion planning tasks in which the robot is holding an object with one hand in order to generate whole-body robot configurations suitable for grasping the object with both hands. In addition, we present two probabilistically complete RRT-based motion planning algorithms (J+-RRT and IK-RRT) that interleave the search for an IK solution with the search for a collision-free trajectory and the extension of these planners to solving re-grasping problems. The capabilities of combining IK methods and planners are shown both in simulation and on the humanoid robot ARMAR-III performing dual-arm tasks in a kitchen environment.

Motion planning for robots with many degrees of freedom (DoF) is a generally unsolved problem in the robotics context. In this work an approach for trajectory planning is presented, which takes account of the different kinematic parts of a humanoid robot. Since not all joints of the robot are important for different planning phases, the RRT-based planner is able to adapt the number of DoF on the fly to improve the performance and the quality of the results. The runtime of the approach is evaluated in comparison to a standard RRT planner. Futhermore several extensions to the algorithm are investigated.

We present an approach to motion planning for highly articulated systems that aims to ensure robust execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By simulating the robot's perception system during search, our planner generates a metric, the so-called perceptive capability, that quantifies the 'sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous manipulations as performed by a humanoid robot in a kitchen environment. Our results indicate that reasoning about the future perceptive capability has the potential to greatly facilitate any task requiring visual feedback during control of the robot manipulator and can thus ensure higher task success rates than perception-unaware planning.

Motion planning for humanoid robotic systems with many degrees of freedom is an important and still generally unsolved problem. To give the robot the ability of acting and navigating in complex environments, the motion planner has to find collision-free paths in a robust manner. The runtime of a planning algorithm is critical, since complex tasks require several planning steps where the collision detection and avoidance should be accomplished in reasonable time. In this paper we present an extension of standard sampling-based techniques using Rapidly Exploring Random Trees (RRT). We extend the free-bubble path validation algorithm from Quinlan, which can be used to guarantee the collision-free status of a C-space path between two samples. By using enlarged robot models it is possible to avoid costly distance calculations and therefore to speed up the planning process. We also present a combined approach based on lazy collision checking that brings together the advantages of fast sampling-based and exact path-validated algorithms. The proposed algorithms have been evaluated by experiments on a humanoid robot in a kitchen environment and by a comparison to a validation based on Quinlan's free bubbles approach.