Author name cluster

Stephen Hart

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6 papers

2 author rows

ICRA Conference 2022 Conference Paper

Generalized Affordance Templates for Mobile Manipulation

Stephen Hart
Ana C. Huamán Quispe
Michael William Lanighan
Seth Gee

This paper presents recent advances to the Affordance Template (AT) task description language. Affordance Templates provide standardized, easy-to-use tools for defining robot manipulation tasks that provide a high level of augmented reality capabilities to facilitate human-in-the-loop operation, but can also be used to support robot autonomy when coupled with various planning tools. While initially defined in terms of end effector waypoint sequences for bimanual robots, such as the NASA Valkyrie and Robonaut 2, this paper extends the original specification to support integrated mobile manipulation, object-centric template definitions, autonomous grasp determination, and integration with custom and off-the-shelf collision free motion planners. ATs have proved highly adaptable to new robots and new domains by both the authors and third-party groups, and have been used in numerous contexts for NASA, DOD, and industry. As such, we believe that the AT framework provides a strong foundation for robot development in multiple real-world contexts that can be increasingly built upon and expanded to meet the challenges of many new applications.

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ICRA Conference 2015 Conference Paper

The Affordance Template ROS package for robot task programming

Stephen Hart
Paul Dinh
Kimberly A. Hambuchen

This paper introduces the Affordance Template ROS package for quickly programming, adjusting, and executing robot applications in the ROS RViz environment. This package extends the capabilities of RViz interactive markers [1] by allowing an operator to specify multiple end-effector waypoint locations and grasp poses in object-centric coordinate frames and to adjust these waypoints in order to meet the run-time demands of the task (specifically, object scale and location). The Affordance Template package stores task specifications in a robot-agnostic JSON description format such that it is trivial to apply a template to a new robot. As such, the Affordance Template package provides a robot-generic ROS tool appropriate for building semi-autonomous, manipulation-based applications. Affordance Templates were developed by the NASA-JSC DARPA Robotics Challenge (DRC) team and have since successfully been deployed on multiple platforms including the NASA Valkyrie and Robonaut 2 humanoids, the University of Texas Dreamer robot and the Willow Garage PR2. In this paper, the specification and implementation of the affordance template package is introduced and demonstrated through examples for wheel (valve) turning, pick-and-place, and drill grasping, evincing its utility and flexibility for a wide variety of robot applications.

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IROS Conference 2014 Conference Paper

Robot Task Commander: A framework and IDE for robot application development

Stephen Hart
Paul Dinh
John D. Yamokoski
Brian Wightman
Nicolaus A. Radford

This paper introduces the Robot Task Commander (RTC) framework for defining, developing, and deploying robot application software for use in different run-time contexts. RTC was created by NASA-JSC in conjunction with General Motors for use with the Robonaut-2 and Valkyrie humanoid robot platforms. RTC provides a robot programming syntax and an IDE appropriate for use by experts and non-experts for implementation and execution. An expert developer can implement a new application with a combination of scripts, called process nodes, and state machines that set the control mode of the robot. A non-expert developer can assemble process nodes and controller state machines into novel hierarchical applications using a visual programming language (VPL). This VPL also allows developers to interface with other RTC applications or with third-party software packages using a variety of network transport mechanisms (ROS, TCP, shared memory, etc.). RTC represents an advantage over other robot programming frameworks by providing multiple levels of flexibility for development. The efficacy of RTC is demonstrated through examples of sophisticated behaviors, such as programming the Valkyrie robot to grab objects and turn a valve.

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ICRA Conference 2008 Conference Paper

Intrinsically motivated hierarchical manipulation

Stephen Hart
Shiraj Sen
Roderic A. Grupen

We present a framework for the programming of manipulation behavior by means of an intrinsic reward function that encourages the building of deep control knowledge. We show how this framework can be used to teach new manipulation skills in a hierarchical and incremental fashion. We demonstrate the contributions of this paper on a humanoid robot through three incremental learning stages.

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IROS Conference 2007 Conference Paper

Natural task decomposition with intrinsic potential fields

Stephen Hart
Roderic A. Grupen

Any given task can be solved in a number of ways, whether through path-planning, modeling, or control techniques. In this paper, we present a methodology for natural task decomposition through the use of intrinsically meaningful potential fields. Specifically, we demonstrate that using classical conditioning measures in a concurrent control framework provides a domain-general means for solving tasks. Among the conditioning measures we use are manipulability [T. Yoshikawam, 1985], localizability [J. Uppala et al. , 2002], and range of motion. To illustrate the value of our approach we demonstrate its applicability to an industrially relevant inspection task.

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AAAI Conference 2005 Conference Paper

A Relational Representation for Procedural Task Knowledge

Stephen Hart

This paper proposes a methodology for learning joint probability estimates regarding the effect of sensorimotor features on the predicated quality of desired behavior. These relationships can then be used to choose actions that will most likely produce success. relational dependency networks are used to learn statistical models of procedural task knowledge. An example task expert for picking up objects is learned through actual experience with a humanoid robot. We believe that this approach is widely applicable and has great potential to allow a robot to autonomously determine which features in the world are salient and should be used to recommend policy for action.

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