Fabio Bonsignorio

Toward Replicable and Measurable Robotics Research [From the Guest Editors]

The articles in this special section focus on robotis measurement techniques. The articles report replicable experiments,benchmarking methods, and a couple of exemplary surveys on competitions.

Fostering Progress in Performance Evaluation and Benchmarking of Robotic and Automation Systems [TC Spotlight]

We have shared benchmarks for many engineering systems and products in the market that can be used to compare solutions and systems. We can compare cars in terms of maximum speed, acceleration, and maximum torque; computers in terms of flops, random access memory, and hard disk capacity; and smartphones in terms of battery life and screen dimensions. We also have shared usability metrics based on human factors, which are used to compare the ease of use of different software interfaces. When we come to the evaluation and the comparison of how intelligent, robust, adaptive, and antifragile the behaviors of robots are in performing a given set of tasks, such as daily life activities with daily life objects such as in a kitchen or a hospital room, we are in trouble.

Task-Oriented Kinematic Design of a Symmetric Assistive Climbing Robot

ASIBOT is an assistive climbing robot that is capable of aiding in daily tasks from fixed docking stations in the environment. A task-oriented design process was applied to improve the robot kinematic structure, which was based on the grid method. Twelve different robot designs were optimized for typical kitchen scenarios, followed by a quantitative comparison.

Quantifying the evolutionary self-structuring of embodied cognitive networks

We outline a possible theoretical framework for the quantitative modeling of networked embodied cognitive systems. We note that: (1) information self-structuring through sensory-motor coordination does not deterministically occur in ℝn vector space, a generic multivariable space, but in SE(3), the group structure of the possible motions of a body in space; (2) it happens in a stochastic open-ended environment. These observations may simplify, at the price of a certain abstraction, the modeling and the design of self-organization processes based on the maximization of some informational measures, such as mutual information. Furthermore, by providing closed form or computationally lighter algorithms, it may significantly reduce the computational burden of their implementation. We propose a modeling framework that aims to give new tools for the design of networks of new artificial self-organizing, embodied, and intelligent agents and for the reverse engineering of natural networks. At this point, it represents largely a theoretical conjecture, and must still to be experimentally verified whether this model will be useful in practice.

Adaptive collision-limitation behavior for an assistive manipulator

An approach for adaptive shared control of an assistive manipulator is presented. A set of distributed collision and proximity sensors is used to aid in limiting collisions during direct control by the disabled user. Artificial neural networks adapt the use of the proximity sensors online, which limits movements in the direction of an obstacle before a collision occurs. The system learns by associating the different proximity sensors to the collision sensors where collisions are detected. This enables the user and the robot to adapt simultaneously and in real-time, with the objective of converging on a usage of the proximity sensors that increases performance for a given user, robot implementation and task-set. The system was tested in a controlled setting with a simulated 5 DOF assistive manipulator and showed promising reductions in the mean time on simplified manipulation tasks. It extends earlier work by showing that the approach can be applied to full multi-link manipulators.

Benchmarking shared control for assistive manipulators: From controllability to the speed-accuracy trade-off

Assistive robots are increasingly being envisioned as an aid to the elderly and disabled. However controlling a robotic system with a potentially large amount of Degrees of Freedom (DOF) in a safe and reliable way is not an easy task, even without limitations in the mobility of the upper extremities. Shared control has been proposed as a way of aiding disabled users in controlling mobility aids such as assistive wheelchairs, by using the sensors of the robotic platform to predict the users intent and assist in navigation. Assistive manipulators, that aim to perform physical Daily Life Activities (DLA), is a more complex problem however. This calls for good experimental practices to ensure repeatability, reproducibility, and steady progress. The work presented here attempts to model the complete system for assistive manipulators, and in the context of this model define metrics and good practices for benchmarking shared control for such robots. An adaptive shared control approach for limiting collisions during teleoperation is used as a case study. Improvements in performance are shown, quantified by the trade-off between mean time and number of collisions as well as the controllability from the users perspective.

Distributed and Adaptive Shared Control Systems: Methodology for the Replication of Experiments

Much work in robotics aimed at real-world applications falls in the large segment between teleoperated and fully autonomous systems. Such systems are characterized by the close coupling between the human operator and the robot, in principle, allowing the agents to share their particular sensing, adaptation, and decision-making capabilities. Replicable experiments can advance the state of the art of such systems but pose practical and epistemological challenges. For example, the trajectory of the system is governed by the adaptation both in the human and the robot agent. What do we need besides (or instead of) data sets for such a system? The degree of similarity between comparable experiments and the exact meaning of replication need to be clarified. Here, we explore replication of a distributed and adaptive shared control for an assistive robot manipulator. We attempt a methodological approach for reporting two virtual human experiments on the system: modeling the complete human-robot binomial, deriving closedloop performance metrics from the models, and openly publishing the results and experiment implementations.

Online learning of sensorimotor interactions using a neural network with time-delayed inputs

The work described here explores an approach for learning online the sensorimotor interaction that a robot has with the world, and the higher-level concepts grounded in this interaction. A type of spatiotemporal connectionist neural network was implemented. In consists of a set of time-delayed input layers which receive both low-level sensor inputs and high-level labels and hypotheses. Each input value activates a range of neurons, based on a Gaussian distribution. A Hebb-like learning rule is used online to associate activations from inputs in the past with activations from inputs in the present. Prediction of future activation is then performed by shifting all inputs one time-step back in time and propagating activation to the present time layers. A simple benchmarking based on a number 8 shape movement with a simulated iCub robot showed good robustness to noise and ambiguity in the trajectories. First results from trials interacting with simulated objects in an imitation learning scenario are also presented. The system was able to learn online and ground labels and hypotheses in the trajectories, although the strength of the predictions was reduced.

An information-theoretic approach to modeling and quantifying assistive robotics HRI

Assistive robotics HRI has a number of important characteristics that distinguishes it from other forms of HRI. This includes the need for both high flexibility, safety and reliability in controlling the robotic system. Approaching the system as a human-robot binomial, with the user and the robot acting in a closed-loop, may be beneficial to understanding and improving the interaction. This paper investigates the feasibility of modeling and quantifying assistive robotics HRI inside such a human-robot binomial using concepts from Information Theory.

Adaptive Aid on Targeted Robot Manipulator Movements in Tele-Assistance

Abstract The teleoperation of robot manipulators over the internet suffers from variable delays in the communications. Here we address a tele-assistance scenario, where a remote operator assists a disabled or elderly user on daily life tasks. Our behavioral approach uses local environment information from robot sensing to help enable faster execution for a given movement tolerance. This is achieved through a controller that automatically slows the operator down before having collisions, using a set of distributed proximity sensors. The controller is made to gradually increase the assistance in situations similar to those where ollisions have occurred in the past, thus adapting to the given operator, robot and task-set. Two controlled virtual experiments for tele-assistance with a 5 DOF manipulator were performed, with 300 ms and 600 ms mean variable round-trip delays. The results showed significant improvements in the median times of 12.6% and 16.5%, respectively. Improvements in the subjective workload were also seen with the controller. A first implementation on a physical robot manipulator is described.