TechTalks from event: Technical session talks from ICRA 2012

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Data Based Learning

  • Improving the Efficiency of Bayesian Inverse Reinforcement Learning Authors: Michini, Bernard; How, Jonathan
    Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given knowledge of the transition function and a set of expert demonstrations. While many IRL algorithms exist, Bayesian IRL [1] provides a general and principled method of reward learning by casting the problem in the Bayesian inference framework. However, the algorithm as originally presented suffers from several inefficiencies that prohibit its use for even moderate problem sizes. This paper proposes modifications to the original Bayesian IRL algorithm to improve its efficiency and tractability in situations where the state space is large and the expert demonstrations span only a small portion of it. The key insight is that the inference task should be focused on states that are similar to those encountered by the expert, as opposed to making the naive assumption that the expert demonstrations contain enough information to accurately infer the reward function over the entire state space. A modified algorithm is presented and experimental results show substantially faster convergence while maintaining the solution quality of the original method.
  • Learning Diffeomorphisms Models of Robotic Sensorimotor Cascades Authors: Censi, Andrea; Murray, Richard
    The problem of bootstrapping consists in designing agents that can learn from scratch the model of their sensorimotor cascade (the series of robot actuators, the external world, and the robot sensors) and use it to achieve useful tasks. In principle, we would want to design agents that can work for any robot dynamics and any robot sensor(s). One of the difficulties of this problem is the fact that the observations are very high dimensional, the dynamics is nonlinear, and there is a wide range of “representation nuisances” to which we would want the agent to be robust. In this paper, we model the dynamics of sensorimotor cascades using diffeomorphisms of the sensel space. We show that this model captures the dynamics of camera and range-finder data, that it can be used for long-term predictions, and that it can capture nonlinear phenomena such as a limited field of view. Moreover, by analyzing the learned diffeomorphisms it is possible to recover the “linear structure” of the dynamics in a manner which is independent of the commands representation.
  • Interactive Generation of Dynamically Feasible Robot Trajectories from Sketches Using Temporal Mimicking Authors: Luo, Jingru; Hauser, Kris
    This paper presents a method for generating dynamically-feasible, natural-looking robot motion from freehand sketches. Using trajectory optimization, it handles sketches that are too fast, jerky, or pass out of reach by enforcing the constraints of the robot’s dynamic limitations while minimizing the relative temporal differences between the robot’s trajectory and the sketch. To make the optimization fast enough for interactive use, a variety of enhancements are employed including decoupling the geometric and temporal optimizations and methods to select good initial trajectories. The technique is also applicable to transferring human motions onto robots with non-human appearance and dynamics, and we use our method to demonstrate a simulated humanoid imitating a golf swing as well as an industrial robot performing the motion of writing a cursive ”hello” word.
  • A Robot Path Planning Framework That Learns from Experience Authors: Berenson, Dmitry; Abbeel, Pieter; Goldberg, Ken
    We propose a framework, called Lightning, for planning paths in high-dimensional spaces that is able to learn from experience, with the aim of reducing computation time. This framework is intended for manipulation tasks that arise in applications ranging from domestic assistance to robot-assisted surgery. Our framework consists of two main modules, which run in parallel: a planning-from-scratch module, and a module that retrieves and repairs paths stored in a path library. After a path is generated for a new query, a library manager decides whether to store the path based on computation time and the generated path's similarity to the retrieved path. To retrieve an appropriate path from the library we use two heuristics that exploit two key aspects of the problem: (i) A correlation between the amount a path violates constraints and the amount of time needed to repair that path, and (ii) the implicit division of constraints into those that vary across environments in which the robot operates and those that do not. We evaluated an implementation of the framework on several tasks for the PR2 mobile manipulator and a minimally-invasive surgery robot in simulation. We found that the retrieve-and-repair module produced paths faster than planning-from-scratch in over 90% of test cases for the PR2 and in 58% of test cases for the minimally-invasive surgery robot.
  • Evaluation of Commonsense Knowledge for Intuitive Robotic Service Authors: Ngo, Trung L.; Lee, Haeyeon; Mayama, Katsuhiro; Mizukawa, Makoto
    Human commonsense is required to improve quality of robotic application. However, to acquire the necessary knowledge, robot needs to evaluate the appropriateness of the data it has collected. This paper presents an evaluation method, by combining the weighting mechanism in commonsense databases with a set of weighting factors. The method was verified on our Basic-level Knowledge Network. We conducted questionnaire to collect a commonsense data set and estimate weighting factors. Result showed that, the proposed method was able to build Robot Technology (RT) Ontology for a smart “Bring something” robotic service. More importantly, it allowed robot to learn new knowledge when necessary. An intuitive human-robot interface application was developed as an example base on our approach.
  • A Temporal Bayesian Network with Application to Design of a Proactive Robotic Assistant Authors: Kwon, Woo Young; Suh, Il Hong
    For effective human-robot interaction, a robot should be able to make prediction about future circumstance. This enables the robot to generate preparative behaviors to reduce waiting time, thereby greatly improving the quality of the interaction. In this paper, we propose a novel probabilistic temporal prediction method for proactive interaction that is based on a Bayesian network approach. In our proposed method, conditional probabilities of temporal events can be explicitly represented by defining temporal nodes in a Bayesian network. Utilizing these nodes, both temporal and causal information can be simultaneously inferred in a unified framework. An assistant robot can use the temporal Bayesian network to infer the best proactive action and the best time to act so that the waiting time for both the human and the robot is minimized. To validate our proposed method, we present experimental results for case in which a robot assists in a human assembly task.

Range Imaging

  • Performance of Histogram Descriptors for the Classification of 3D Laser Range Data in Urban Environments Authors: Behley, Jens; Steinhage, Volker; Cremers, Armin
    The selection of suitable features and their parameters for the classification of three-dimensional laser range data is a crucial issue for high-quality results. In this paper we compare the performance of different histogram descriptors and their parameters on three urban datasets recorded with various sensors—sweeping SICK lasers, tilting SICK lasers and a Velodyne 3D laser range scanner. These descriptors are 1D, 2D, and 3D histograms capturing the distribution of normals or points around a query point. We also propose a novel histogram descriptor, which relies on the spectral values in different scales. We argue that choosing a larger support radius and a z-axis based global reference frame/axis can boost the performance of all kinds of investigated classification models significantly. The 3D histograms relying on the point distribution, normal orientations, or spectral values, turned out to be the best choice for the classification in urban environments.
  • Exploiting Segmentation for Robust 3D Object Matching Authors: Krainin, Michael; Konolige, Kurt; Fox, Dieter
    While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter, both beam-based and ICP approaches can fail to find good solutions. In this paper, we present both an optimization method for beam-based models and a novel framework for modeling observation dependencies in beam-based models using over-segmentations. This technique enables reasoning about object extents and works well in heavy clutter. We also make available a ground-truth 3D dataset for testing algorithms in this area.
  • Segmenting "Simple" Objects Using RGB-D Authors: Mishra, Ajay; Shrivastava, Ashish; Aloimonos, Yiannis
    Segmenting “simple” objects using low-level visual cues is an important capability for a vision system to learn in an unsupervised manner. We define a “simple” object as a compact region enclosed by depth and/or contact boundary in the scene. We propose a segmentation process to extract all the “simple” objects that builds on the fixation-based segmentation framework [13] that segments a region given a point anywhere inside it. In this work, we augment that framework with a fixation strategy to automatically select points inside the “simple” objects and a postsegmentation process to select only the regions corresponding to the “simple” objects in the scene. A novel characteristic of our approach is the incorporation of border ownership, the knowledge about the object side of a boundary pixel. We evaluate the process on a RGB-D dataset [9] and finds that it successfully extracts 91.4% of all objects in the scene.
  • Sparse Online Low-Rank Projection and Outlier Rejection (SOLO) for 3-D Rigid-Body Motion Registration Authors: Yang, Allen; Slaughter, Chris; Bagwell, Justin; Checkles, Costa; Sentis, Luis; Vishwanath, Sriram
    Motivated by an emerging theory of robust low-rank matrix representation, in this paper, we introduce a novel solution for online rigid-body motion registration. The goal is to develop algorithmic techniques that enable a robust, real-time motion registration solution suitable for low-cost, portable 3-D camera devices. Assuming 3-D image features are tracked via a standard tracker, the algorithm first utilizes Robust PCA to initialize a low-rank shape representation of the rigid body. Robust PCA finds the global optimal solution of the initialization, while its complexity is comparable to singular value decomposition. In the online update stage, we propose a more efficient algorithm for sparse subspace projection to sequentially project new feature observations onto the shape subspace. The lightweight update stage guarantees the real-time performance of the solution while maintaining good registration even when the image sequence is contaminated by noise, gross data corruption, outlying features, and missing data. The state-of-the-art accuracy of the solution is validated through extensive simulation and a real-world experiment, while the system enjoys one to two orders of magnitude speed-up compared to well-established RANSAC solutions.
  • An Integrated 2D and 3D Location Measurement System Using Spiral Motion Positioner Authors: Lee, Geunho; Noguchi, Naoto; Kawasaki, Nobuya; Chong, Nak Young
    In this paper, we describe the design and implementation of an integrated two dimensional and three dimensional location measurement system, where different types of range sensors can be mounted onto the spiral motion positioner. The proposed sensor/positioner system enables terrestrial and aerial robots to observe their surroundings in all directions without blind spots. Using a nut-and-bolt and link mechanism, the proposed positioner driven by a single stepper motor exhibits continuous three dimensional spiral trajectories over the upper hemisphere. This single axis motor driven system helps decrease the size, weight, and structural complexity of the system. Particular attention in this work is placed on how to effectively combine two dimensional and three dimensional measurement functions. We verify the validity and effectiveness of the proposed location measurement system through simulations and experiments. It is expected that the proposed system can be incorporated into a wide range of mobile robot platforms.
  • An Occlusion-aware Feature for Range Images Authors: Quadros, Alastair James; Underwood, James Patrick; Douillard, Bertrand
    This paper presents a novel local feature for 3D range image data called `the line image'. It is designed to be highly viewpoint invariant by exploiting the range image to efficiently detect 3D occupancy, producing a representation of the surface, occlusions and empty spaces. We also propose a strategy for defining keypoints with stable orientations which define regions of interest in the scan for feature computation. The feature is applied to the task of object classification on sparse urban data taken with a Velodyne laser scanner, producing good results.

Collision

  • Time-Optimal Multi-Stage Motion Planning with Guaranteed Collision Avoidance Via an Open-Loop Game Formulation Authors: Takei, Ryo; Huang, Haomiao; Ding, Jerry; Tomlin, Claire
    We present an efficient algorithm which computes, for a kinematic point mass moving in the plane, a time-optimal path that visits a sequence of target sets while conservatively avoiding collision with moving obstacles, also modelled as kine- matic point masses, but whose trajectories are unknown. The problem is formulated as a pursuit-evasion differential game, and the underlying construction is based on optimal control. The algorithm, which is a variant of the fast marching method for shortest path problems, can handle general dynamical constraints on the players and arbitrary domain geometry (e.g. obstacles, non-polygonal boundaries). Applications to a two- stage game, capture-the-flag, is presented.
  • Execution and Analysis of High-Level Tasks with Dynamic Obstacle Anticipation Authors: Johnson, Benjamin; Havlak, Frank; Campbell, Mark; Kress-Gazit, Hadas
    This paper uniquely embeds high-level robot controllers with sensor data obtained from abstracting probabilistic anticipation of the behavior of dynamic obstacles. An example problem of an autonomous vehicle operating in an urban environment, in the presence of other vehicles and pedestrians, is used as motivation. The correct-by-construction controller is automatically synthesized from a set of high-level tasks, specified as temporal logic formulas. The anticipated behavior of other vehicles is abstracted to a set of propositions describing the safety of road segments at intersections, and used as the output of high-level sensors for the controller. Such an input to the controller is inherently probabilistic, and this paper investigates the types of probabilistic guarantees that can be made about the system using both formal and statistical analysis.
  • A Depth Space Approach to Human-Robot Collision Avoidance Authors: Flacco, Fabrizio; Kroeger, Torsten; De Luca, Alessandro; Khatib, Oussama
    In this paper a real-time collision avoidance approach is presented for safe human-robot coexistence. The main contribution is a fast method to evaluate distances between the robot and possibly moving obstacles (including humans), based on the concept of depth space. With these distances, repulsive vectors are generated that are used to control the robot while executing a generic motion task. The repulsive vectors can also take advantage of an estimation of the obstacle velocity. In order to preserve the execution of a Cartesian task with a redundant manipulator, a simple collision avoidance algorithm has been implemented where different reaction behaviors are set up for the end-effector and for other control points along the robot structure. The complete collision avoidance framework, from perception of the environment to joint-level robot control, is presented for a 7-dof KUKA Light-Weight-Robot IV using the Microsoft Kinect sensor. Experimental results are reported for dynamic environments with obstacles and a human.
  • LQG-Obstacles: Feedback Control with Collision Avoidance for Mobile Robots with Motion and Sensing Uncertainty Authors: van den Berg, Jur; Wilkie, David; Guy, Stephen J.; Niethammer, Marc; Manocha, Dinesh
    This paper presents LQG-Obstacles, a new concept that combines linear-quadratic feedback control of mobile robots with guaranteed avoidance of collisions with obstacles. Our approach generalizes the concept of Velocity Obstacles to any robotic system with a linear Gaussian dynamics model. We integrate a Kalman filter for state estimation and an LQR feedback controller into a closed-loop dynamics model of which a higher-level control objective is the ``control input''. We then define the LQG-Obstacle as the set of control objectives that result in a collision with high probability. Selecting a control objective outside the LQG-Obstacle then produces collision-free motion. We demonstrate the potential of LQG-Obstacles by safely and smoothly navigating a simulated quadrotor helicopter with complex non-linear dynamics and motion and sensing uncertainty through three-dimensional environments with obstacles and narrow passages.
  • K-IOS: Intersection of Spheres for Efficient Proximity Query Authors: Zhang, Xinyu; Kim, Young J.
    We present a new bounding volume structure, k-IOS that is an intersection of k spheres, for accelerating proximity query including collision detection and Euclidean distance computation between arbitrary polygon-soup models that undergo rigid motion. Our new bounding volume is easy to implement and highly efficient both for its construction and runtime query. In our experiments, we have observed up to 4.0 times performance improvement of proximity query compared to an existing well-known algorithm based on swept sphere volume (SSV) [1]. Moreover, k-IOS is strictly convex that can guarantee a continuous gradient of distance function with respect to object’s configuration parameter.
  • Reciprocal Collision Avoidance for Multiple Car-Like Robots Authors: Alonso-Mora, Javier; Breitenmoser, Andreas; Beardsley, Paul; Siegwart, Roland
    In this paper a method for distributed reciprocal collision avoidance among multiple non-holonomic robots with bike kinematics is presented. The proposed algorithm, bicycle reciprocal collision avoidance (B-ORCA), builds on the concept of optimal reciprocal collision avoidance (ORCA) for holonomic robots but furthermore guarantees collision-free motions under the kinematic constraints of car-like vehicles. The underlying principle of the B-ORCA algorithm applies more generally to other kinematic models, as it combines velocity obstacles with generic tracking control. The theoretical results on collision avoidance are validated by several simulation experiments between multiple car-like robots.