A theoretical framework for grounding language is introduced that provides a computational path from sensing and motor action to words and speech acts. The approach combines concepts from semiotics and schema theory to develop a holistic approach to linguistic meaning. Schemas serve as structured beliefs that are grounded in an agent’s physical environment through a causal-predictive cycle of action and perception. Words and basic speech acts are interpreted in terms of grounded schemas. The framework reflects lessons learned from implementations of several language processing robots. It provides a basis for the analysis and design of situated, multimodal communication systems that straddle symbolic and non-symbolic realms.

We consider the problem of grasping novel objects, specifically ones that are being seen for the first time through vision. Grasping a previously unknown object, one for which a 3-d model is not available, is a challenging problem. Further, even if given a model, one still has to decide where to grasp the object. We present a learning algorithm that neither requires, nor tries to build, a 3-d model of the object. Given two (or more) images of an object, our algorithm attempts to identify a few points in each image corresponding to good locations at which to grasp the object. This sparse set of points is then triangulated to obtain a 3-d location at which to attempt a grasp. This is in contrast to standard dense stereo, which tries to triangulate every single point in an image (and often fails to return a good 3-d model). Our algorithm for identifying grasp locations from an image is trained via supervised learning, using synthetic images for the training set. We demonstrate this approach on two robotic manipulation platforms. Our algorithm successfully grasps a wide variety of objects, such as plates, tape-rolls, jugs, cellphones, keys, screwdrivers, staplers, a thick coil of wire, a strangely shaped power horn, and others, none of which were seen in the training set. We also apply our method to the task of unloading items from dishwashers. 1 1

Much of the tradition in robot grasping is rooted in geometrical, planning-based approaches in which it is assumed that object and nger geometries are well modeled a priori. Some recent approaches have chosen instead to deal with objects of unknown geometry. These techniques treat grasping as an active sensorydriven problem. At any given time, nger contacts are incrementally displaced along the object's local surface using a single control law. In this paper, we extend this approach by allowing multiple control laws to be active simultaneously. Three control laws are combined by projecting the actions of subordinate control laws into other control law nullspaces. The resulting composite controller nds grasps that are more robust than the component primitives in isolation. Finally, we show how this approach may be used on hand/arm manipulation systems with arbitrary kinematics.

Abstract — We describe a probabilistic framework for detection and modeling of doors from sensor data acquired in corridor environments with mobile robots. The framework captures shape, color, and motion properties of door and wall objects. The probabilistic model is optimized with a version of the expectation maximization algorithm, which segments the environment into door and wall objects and learns their properties. The framework allows the robot to generalize the properties of detected object instances to new object instances. We demonstrate the algorithm on real-world data acquired by a Pioneer robot equipped with a laser range finder and an omni-directional camera. Our results show that our algorithm reliably segments the environment into walls and doors, finding both doors that move and doors that do not move. We show that our approach achieves better results than models that only capture behavior, or only capture appearance. I.

Abstract — Robots that work alongside us in our homes and workplaces could extend the time an elderly person can live at home, provide physical assistance to a worker on an assembly line, or help with household chores. In order to assist us in these ways, robots will need to successfully perform manipulation tasks within human environments. Human environments present special challenges for robot manipulation since they are complex, dynamic, uncontrolled, and difficult to perceive reliably. In this paper we present a behavior-based control system that enables a humanoid robot, Domo, to help a person place objects on a shelf. Domo is able to physically locate the shelf, socially cue a person to hand it an object, grasp the object that has been handed to it, transfer the object to the hand that is closest to the shelf, and place the object on the shelf. We use this behavior-based control system to illustrate three themes that characterize our approach to manipulation in human environments. The first theme, cooperative manipulation, refers to the advantages that can be gained by having the robot work with a person to cooperatively perform manipulation tasks. The second theme, task relevant features, emphasizes the benefits of carefully selecting the aspects of the world that are to be perceived and acted upon during a manipulation task. The third theme, let the body do the thinking, encompasses several ways in which a robot can use its body to simplify manipulation tasks. 1 Fig. 1. The humanoid robot Domo used in this paper. I.

We consider the problem of grasping novel objects in cluttered environments. If a full 3-d model of the scene were available, one could use the model to estimate the stability and robustness of different grasps (formalized as form/force-closure, etc); in practice, however, a robot facing a novel object will usually be able to perceive only the front (visible) faces of the object. In this paper, we propose an approach to grasping that estimates the stability of different grasps, given only noisy estimates of the shape of visible portions of an object, such as that obtained from a depth sensor. By combining this with a kinematic description of a robot arm and hand, our algorithm is able to compute a specific positioning of the robot’s fingers so as to grasp an object. We test our algorithm on two robots (with very different arms/manipulators, including one with a multi-fingered hand). We report results on the task of grasping objects of significantly different shapes and appearances than ones in the training set, both in highly cluttered and in uncluttered environments. We also apply our algorithm to the problem of unloading items from a dishwasher.

Abstract — We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a generalpurpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations— partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels. I.

Abstract. One of the goals of humanoid research is the development of a humanoid capable of performing useful tasks in unknown or unpredictable environments. To address the complexities of this task, the robot must continually accumulate and utilize new control and perceptual knowledge. In this paper, we present a control framework for accomplishing this. Robot control policies can be learned at different levels of abstraction. We show how task-relevant perceptual features can be discovered that make better control policies possible. We also explore how trajectories of closed-loop policies can provide uniquely relevant state information. The approach presented in this paper is illustrated with several case studies on actual robot systems. 1

Abstract — A system is detailed here for using imitation learning to teach a robot to grasp objects using both hand and wholebody grasps, which use the arms and torso as well as hands. Demonstration grasp trajectories are created by teleoperating a simulated robot to pick up simulated objects, modeled as combinations of up to three aligned primitives—boxes, cylinders, and spheres. When presented with a target object, the system compares it against the objects in a stored database to pick a demonstrated grasp used on a similar object. By considering the target object to be a transformed version of the demonstration object, contact points are mapped from one object to the other. The most promising grasp candidate is chosen with the aid of a grasp quality metric. To test the success of the chosen grasp, a collision-free grasp trajectory is found and an attempt is made to execute it in simulation. The implemented system successfully picks up 92 out of 100 randomly generated test objects in simulation. I.

Humans learn robust and efficient strategies for visual tasks through interaction with their environment. In contrast, most current computer vision systems have no such learning capabilities. Motivated by insights from psychology and neurobiology, I combine machine learning and computer vision techniques to develop algorithms for visual learning in open-ended tasks. Learning is incremental and makes only weak assumptions about the task environment. I begin