From embodied to socially embedded agents - Implications for interaction-aware robots

This work contributes to the development of a common framework for the discussion and analysis of dexterous manipulation across the human and robotic domains. An overview of previous work is first provided along with an analysis of the tradeoffs between arm and hand dexterity. A hand-centric and motion-centric manipulation classification is then presented and applied in four different ways. It is first discussed how the taxonomy can be used to identify a manipulation strategy. Then, applications for robot hand analysis and engineering design are explained. Finally, the classification is applied to three activities of daily living (ADLs) to distinguish the patterns of dexterous manipulation involved in each task. The same analysis method could be used to predict problem ADLs for various impairments or to produce a representative benchmark set of ADL tasks. Overall, the classification scheme proposed creates a descriptive framework that can be used to effectively describe hand movements during manipulation in a variety of contexts and might be combined with existing object centric or other taxonomies to provide a complete description of a specific manipulation task.

/pdf/a-hand-centric-classification-of-human-and-robot-dexterous-pu5pw9m1mv.pdf

A Hand-Centric Classification of Human and Robot Dexterous Manipulation

A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.

Skill acquisition from human demonstration using a hidden Markov model

This paper introduces a modular framework for robot motion control using stereo vision. The approach is based on a small number of generic motion control operations referred to as primitive skills. Each primitive skill uses visual feedback to enforce a specific task-space kinematic constraint between a robot end-effector and a set of target features. By observing both the end-effector and target features, primitive skills are able to position with an accuracy that is independent of errors in hand-eye calibration. Furthermore, primitive skills are easily combined to form more complex kinematic constraints as required by different applications. These control laws have been integrated into a system that performs tracking and control on a single processor at real-time rates. Experiments with this system have shown that it is extremely accurate, and that it is insensitive to camera calibration error. The system has been applied to a number of example problems, showing that modular, high precision, vision-based motion control is easily achieved with off-the-shelf hardware.

/pdf/a-modular-system-for-robust-positioning-using-feedback-from-1hogd5h26s.pdf

A modular system for robust positioning using feedback from stereo vision

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications ranging from scouting and reconnaissance, to search and rescue, to manipulation tasks, to cooperative localization and mapping, and formation control. Our software framework allows a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex software systems. We demonstrate the algorithms and software on an experimental testbed that involves a group of carlike robots, each using a single omnidirectional camera as a sensor without explicit use of odometry.

/pdf/a-framework-and-architecture-for-multi-robot-coordination-4q543v5h3q.pdf

A Framework and Architecture for Multi-Robot Coordination

A manipulation task primitive is classified by the relative motion between two (rigid) parts. Only twenty different relative motions are possible and these can be used to guide the identification and development of manipulation task primitives. The goal is to build a richer library of robot capabilities in the manipulation domain. By identifying manipulation task primitives and instantiating solutions to them with available sensors and robot hardware in the form of sensorimotor primitives, we provide a higher-level abstraction for composing solutions to complex manipulation tasks. A key benefit is the ability to re-use costly sensor-based control algorithms for executing these primitives. We discuss the implementation of a few manipulation task primitives using force damping control and active vision feedback. Finally, we decompose a common task into two different skills using the primitives described.

Manipulation task primitives for composing robot skills

We present a new technique for multi-axis force/torque sensor calibration called shape from motion. The novel aspect of this technique is that it does not require explicit knowledge of the redundant applied load vectors, yet it retains the noise rejection of a highly redundant data set and the rigor of least squares. The result is a much faster, slightly more accurate calibration procedure. A constant-magnitude force (produced by a mass in a gravity field) is randomly moved through the sensing space while raw data is continuously gathered. Using only the raw sensor signals, the motion of the force vector (the motion) and the calibration matrix (the shape) are simultaneously extracted by singular value decomposition. We have applied this technique to several types offorce/torque sensors and present experimental results for a 2-DOF fingertip and a 6-DOF wrist sensor with comparisons to the standard least squares approach.

/pdf/the-shape-from-motion-approach-to-rapid-and-precise-force-31zj3503j0.pdf

The Shape from Motion Approach to Rapid and Precise Force/Torque Sensor Calibration

Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these methods, there arises a need for careful validation, particularly for physically agnostic models-that is, for potentials that extract the nature of atomic interactions from reference data. Here, we review the basic principles behind ML potentials and their validation for atomic-scale material modeling. We discuss the best practice in defining error metrics based on numerical performance, as well as physically guided validation. We give specific recommendations that we hope will be useful for the wider community, including those researchers who intend to use ML potentials for materials "off the shelf."

https://aip.scitation.org/doi/pdf/10.1063/5.0139611

How to validate machine-learned interatomic potentials.

Force controlled manipulation is a common technique for compliantly contacting and manipulating uncertain environments. Visual servoing is effective for reducing alignment uncertainties between objects using imprecisely calibrated camera-lens-manipulator systems. These two types of manipulator feedback, force and vision, represent complementary sensing modalities; visual feedback provides information over a relatively large area of the workspace without requiring contact with the environment, and force feedback provides highly localized and precise information upon contact. This paper presents three different strategies which combine force and vision within the feedback loop of a manipulator, traded control, hybrid control, and shared control. A discussion of the types of tasks that benefit from the strategies is included, as well as experimental results which show that the use of visual servoing to stably guide a manipulator simplifies the force control problem by allowing the effective use of low gain force control with relatively large stability margins.

/pdf/improved-force-control-through-visual-servoing-1udrr4fc3c.pdf

Improved force control through visual servoing

http://www.ri.cmu.edu/pub_files/pub3/nelson_bradley_1995_1/nelson_bradley_1995_1.pdf

Robotic manipulation using high bandwidth force and vision feedback

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