Topic: estimation, prediction Oral presentation or poster presentationHome environments are envisioned as one of the key application areas for service robots. Robots operating in such environments are typically faced with a variety objects they have to deal with or to manipulate to fulfill a given task. Many objects are not rigid since they have moving parts such as drawers or doors. Understanding the spatial movements of parts of such objects is essential for service robots to allow them to plan relevant actions such as door-opening trajectories. Ideally, robots are able to autonomously infer these articulation models by observation. In this work, we therefore investigate the problem of learning kinematic models of articulated objects from observations. As an illustrating example, consider the left three images of Figure 1 which depict two examples for observations of the door of a microwave oven and a learned, one-dimensional description of the door motion.Our problem can be formulated as follows: Given a sequence of rigid body poses from observed objects parts, learn a compact kinematic model describing the whole articulated object. This kinematic model has to define (i) which parts are connected, (ii) the dimensionality of the latent (not observed) actuation space of the object, and (iii) a kinematic function between different body parts in a generative way allowing a robot to reason also about unseen configurations. Our approach is related to recent work of Katz et al. [1] who learn planar kinematic models for articulated objects such as scissors by manipulating the object as well as to the work of Yan and Pollefeys [4] who present an approach for learning the structure of an articulated object from feature trajectories under affine projections.The contribution of this work is a novel approach for learning actuation models based on observations only. Our method is able to robustly detect the connectivity of the rigid parts of the object and to estimate accurate articulation models from a candidate set. Our approach allows for selecting the best model among parametric, expert-designed transformation templates (rotational and prismatic models), and nonparametric transformations that are learned from scratch requiring minimal prior assumptions. To obtain a parameter-free description, we apply Gaussian processes [2] as a non-parametric regression technique to learn flexible and accurate models. To find the low-dimensional description of the moving parts, we furthermore apply local linear embedding [3], which is a non-linear dimensionality reduction technique.We implemented our approach on a real robot and tested it by estimating models of different objects, including a door of a microwave oven (see Figure 1 left), a cabinet with drawers (see Figure 1 right), a garage door (see Figure 2), and a table moved on the ground plane (see Figure 3). Our technique allows to learn accurate models for different articulated objects. We regard this as an important step towards autonomous robots understanding and actively ha...
SummaryIn recent years the Robot Operating System (Quigley et al. 2009) (ROS) has become the 'de facto' standard framework for robotics software development. The ros_control framework provides the capability to implement and manage robot controllers with a focus on both real-time performance and sharing of controllers in a robot-agnostic way. The primary motivation for a sepate robot-control framework is the lack of realtimesafe communication layer in ROS. Furthermore, the framework implements solutions for controller-lifecycle and hardware resource management as well as abstractions on hardware interfaces with minimal assumptions on hardware or operating system. The clear, modular design of ros_control makes it ideal for both research and industrial use and has indeed seen many such applications to date. The idea of ros_control originates from the pr2_controller_manager framework specific to the PR2 robot but ros_control is fully robot-agnostic. Controllers expose standard ROS interfaces for out-of-the box 3rd party solutions to robotics problems like manipulation path planning (MoveIt! (Chitta, Sucan, and Cousins 2012)) and autonomous navigation (the ROS navigation stack). Hence, a robot made up of a mobile base and an arm that support ros_control doesn't need any additional code to be written, only a few controller configuration files and it is ready to navigate autonomously and do path planning for the arm. ros_control also provides several libraries to support writing custom controllers.
This paper focuses on optimal path planning for vehicles in known environments. Previous work has presented mixed integer linear programming (MILP) formulations, which suffer from scalability issues as the number of obstacles, and hence the number of integer variables, increases. In order to address MILP scalability, a novel three-stage algorithm is presented which first computes a desirable path through the environment without considering dynamics, then generates a sequence of convex polytopes containing the desired path, and finally poses a MILP to identify a suitable dynamically feasible path. The sequence of polytopes form a safe tunnel through the environment, and integer decision variables are restricted to deciding when to enter and exit each region of the tunnel. Simulation results for this approach are presented and reveal a significant increase in the size and complexity of the environment that can be solved.
Abstract-We present a series of experiments which explore the use of consumer-grade accelerometers as joint position sensors for robotic manipulators. We show that 6-and 7-dof joint angle estimation is possible by using one 3-d accelerometer for each pair of joints. We demonstrate two calibration approaches and experimental results using accelerometer-based control in both position-control and torque-control regimes. We present a manipulator design combining accelerometer-based sensing with low-cost actuation, and conclude by demonstrating the utility of consumer-grade accelerometers even on high-precision manipulators.
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