Roderic A. Grupen

THE APPLICATIONS OF HARMONIC-FUNCTIONS TO ROBOTICS

Harmonic functions are solutions to Laplaces equation. Such functions can be used to advantage for potential-field path planning because they do not exhibit spurious local minima. Harmonic functions are shown here to have a number of properties that are essential to robotics applications. Paths derived from harmonic functions are generally smooth. Harmonic functions also offer a complete path-planning algorithm. We show how a harmonic function can be used as the basis for a reactive admittance control. Such schemes allow incremental updating of the environment model. Methods for computing harmonic functions respond well to sensed changes in the environment, and can be used for control while the environment model is being updated.

Robot learning from demonstration by constructing skill trees

We describe CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration trajectory into a chain of component skills, where each skill has a goal and is assigned a suitable abstraction from an abstraction library. These properties permit skills to be improved efficiently using a policy learning algorithm. Chains from multiple demonstration trajectories are merged into a skill tree. We show that CST can be used to acquire skills from human demonstration in a dynamic continuous domain, and from both expert demonstration and learned control sequences on the uBot-5 mobile manipulator.

Developing Haptic and Visual Perceptual Categories for Reaching and Grasping with a Humanoid Robot

Abstract Properties of the human embodiment — sensorimotor apparatus and neurological structure — participate directly in the growth and development of cognitive processes against enormous worst case complexity. It is our position that relationships between morphology and perception over time lead to increasingly comprehensive models that describe the agent’s relationship to the world. We are applying insight derived from neuroscience, neurology, and developmental psychology to the design of advanced robot architectures. To investigate developmental processes, we have begun to approximate the human sensorimotor configuration and to engage sensory and motor subsystems in developmental sequences. Many such sequences have been documented in studies of infant development, so we intend to bootstrap cognitive structures in robots by emulating some of these growth processes that bear an essential resemblance to the human morphology. In this paper, we will show two related examples in which a humanoid robot determines the models and representations that govern its behavior. The first is a model that captures the dynamics of a haptic exploration of an object with a dextrous robot hand that supports skillful grasping. The second example constructs constellations of visual features to predict relative hand/object postures that lead reliably to haptic utility. The result is a first step in a trajectory toward associative visual-haptic categories that bounds the incremental complexity of each stage of development.

Nullspace composition of control laws for grasping

Much of the tradition in robot grasping is rooted in geometrical, planning-based approaches in which it is assumed that object 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 sensory-driven problem. At any given time, finger contacts are incrementally displaced along the objects 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 finds 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.

Learning reactive admittance control

A peg-in-hole insertion task is used as an example to illustrate the utility of direct associative reinforcement learning methods for learning control under real-world conditions of uncertainty and noise. An associative reinforcement learning system has to learn appropriate actions in various situations through a search guided by evaluative performance feedback The authors used such a learning system, implemented as a connectionist network, to learn active compliant control for peg-in-hole insertion. The results indicated that direct reinforcement learning can be used to learn a reactive control strategy that works well even in the presence of a high degree of noise and uncertainty.<<ETX>>

A feedback control structure for on-line learning tasks

Abstract This paper addresses adaptive control architectures for systems that respond autonomously to changing tasks. Such systems often have many sensory and motor alternatives and behavior drawn from these produces varying quality solutions. The objective is then to ground behavior in control laws which, combined with resources, enumerate closed-loop behavioral alternatives. Use of such controllers leads to analyzable and predictable composite systems, permitting the construction of abstract behavioral models. Here, discrete event system and reinforcement learning techniques are employed to constrain the behavioral alternatives and to synthesize behavior on-line. To illustrate this, a quadruped robot learning a turning gait subject to safety and kinematic constraints is presented.

Mobile manipulators for assisted living in residential settings

Abstract We describe a methodology for creating new technologies for assisted living in residential environments. The number of eldercare clients is expected to grow dramatically over the next decade as the baby boom generation approaches 65 years of age. The UMass/Smith ASSIST framework aims to alleviate the strain on centralized medical providers and community services as their clientele grow, reduce the delays in service, support independent living, and therefore, improve the quality of life for the up-coming elder population. We propose a closed loop methodology wherein innovative technical systems are field tested in assisted care facilities and analyzed by social scientists to create and refine residential systems for independent living. Our goal is to create technology that is embraced by clients, supports efficient delivery of support services, and facilitates social interactions with family and friends. We introduce a series of technologies that are currently under evaluation based on a distributed sensor network and a unique mobile manipulator (MM) concept. The mobile manipulator provides client services and serves as an embodied interface for remote service providers. As a result, a wide range of cost-effective eldercare applications can be devised, several of which are introduced in this paper. We illustrate tools for social interfaces, interfaces for community service and medical providers, and the capacity for autonomous assistance in the activities of daily living. These projects and others are being considered for field testing in the next cycle of ASSIST technology development.

A model of shared grasp affordances from demonstration

This paper presents a hierarchical, statistical topic model for representing the grasp preshapes of a set of objects. Observations provided by teleoperation are clustered into latent affordances shared among all objects. Each affordance defines a joint distribution over position and orientation of the hand relative to the object and conditioned on visual appearance. The parameters of the model are learned using a Gibbs sampling method. After training, the model can be used to compute grasp preshapes for a novel object based on its visual appearance. The model is evaluated experimentally on a set of objects for its ability to generate grasp preshapes that lead to successful grasps, and compared to a baseline approach.

Manipulation gaits: sequences of grasp control tasks

In dexterous manipulation, an object must be reconfigured while maintaining a stable grasp. This may require that the object be re-grasped in order to avoid finger workspace limits. We present a set of closed-loop controllers designed to achieve force-related objectives such as wrench closure, and show how they may be concurrently combined. Furthermore, we show that dexterous manipulation behavior may be generated by sequencing concurrent combinations of these controllers. We show that dexterous manipulation can be viewed as a task that is accomplished in the context of a wrench closure constraint. We hypothesize this approach can generalize to any task that must be accomplished while maintaining a set of constraints.

Tracing patterns and attention: humanoid robot cognition

Humanoid robots promise to lead us toward more effective and informative interactions between humans and robotic devices. The authors introduce mechanisms for attention control and pattern categorization as the basis for cognition in a humanoid robot.