Toward Enabling Safe & Efficient Human-Robot Manipulation in Shared Workspaces

When humans interact, there are many avenues of physical communication available ranging from vocal to physical gestures. In our past observations, when humans collaborate on manipulation tasks in shared workspaces there is often minimal to no verbal or physical communication, yet the collaboration is still fluid with minimal interferences between partners. However, when humans perform similar tasks in the presence of a robot collaborator, manipulation can be clumsy, disconnected, or simply not human-like. The focus of this work is to leverage our observations of human-human interaction in a robot\u27s motion planner in order to facilitate more safe, efficient, and human-like collaborative manipulation in shared workspaces. We first present an approach to formulating the cost function for a motion planner intended for human-robot collaboration such that robot motions are both safe and efficient. To achieve this, we propose two factors to consider in the cost function for the robot\u27s motion planner: (1) Avoidance of the workspace previously-occupied by the human, so robot motion is safe as possible, and (2) Consistency of the robot\u27s motion, so that the motion is predictable as possible for the human and they can perform their task without focusing undue attention on the robot. Our experiments in simulation and a human-robot workspace sharing study compare a cost function that uses only the first factor and a combined cost that uses both factors vs. a baseline method that is perfectly consistent but does not account for the human\u27s previous motion. We find using either cost function we outperform the baseline method in terms of task success rate without degrading the task completion time. The best task success rate is achieved with the cost function that includes both the avoidance and consistency terms. Next, we present an approach to human-attention aware robot motion generation which attempts to convey intent of the robot\u27s task to its collaborator. We capture human attention through the combined use of a wearable eye-tracker and motion capture system. Since human attention isn\u27t static, we present a method of generating a motion policy that can be queried online. Finally, we show preliminary tests of this method

SLOT-V: Supervised Learning of Observer Models for Legible Robot Motion Planning in Manipulation

We present SLOT-V, a novel supervised learning framework that learns observer models (human preferences) from robot motion trajectories in a legibility context. Legibility measures how easily a (human) observer can infer the robot's goal from a robot motion trajectory. When generating such trajectories, existing planners often rely on an observer model that estimates the quality of trajectory candidates. These observer models are frequently hand-crafted or, occasionally, learned from demonstrations. Here, we propose to learn them in a supervised manner using the same data format that is frequently used during the evaluation of aforementioned approaches. We then demonstrate the generality of SLOT-V using a Franka Emika in a simulated manipulation environment. For this, we show that it can learn to closely predict various hand-crafted observer models, i.e., that SLOT-V's hypothesis space encompasses existing handcrafted models. Next, we showcase SLOT-V's ability to generalize by showing that a trained model continues to perform well in environments with unseen goal configurations and/or goal counts. Finally, we benchmark SLOT-V's sample efficiency (and performance) against an existing IRL approach and show that SLOT-V learns better observer models with less data. Combined, these results suggest that SLOT-V can learn viable observer models. Better observer models imply more legible trajectories, which may - in turn - lead to better and more transparent human-robot interaction

The visual preferences for forest regeneration and field afforestation : four case studies in Finland

The overall aim of this dissertation was to study the public's preferences for forest regeneration fellings and field afforestations, as well as to find out the relations of these preferences to landscape management instructions, to ecological healthiness, and to the contemporary theories for predicting landscape preferences. This dissertation includes four case studies in Finland, each based on the visualization of management options and surveys. Guidelines for improving the visual quality of forest regeneration and field afforestation are given based on the case studies. The results show that forest regeneration can be connected to positive images and memories when the regeneration area is small and some time has passed since the felling. Preferences may not depend only on the management alternative itself but also on the viewing distance, viewing point, and the scene in which the management options are implemented. The current Finnish forest landscape management guidelines as well as the ecological healthiness of the studied options are to a large extent compatible with the public's preferences. However, there are some discrepancies. For example, the landscape management instructions as well as ecological hypotheses suggest that the retention trees need to be left in groups, whereas people usually prefer individually located retention trees to those trees in groups. Information and psycho-evolutionary theories provide some possible explanations for people's preferences for forest regeneration and field afforestation, but the results cannot be consistently explained by these theories. The preferences of the different stakeholder groups were very similar. However, the preference ratings of the groups that make their living from forest - forest owners and forest professionals - slightly differed from those of the others. These results provide support for the assumptions that preferences are largely consistent at least within one nation, but that knowledge and a reference group may also influence preferences.Väitöskirjassa tutkittiin ihmisten maisemapreferenssejä (maisemallisia arvostuksia) metsänuudistamishakkuiden ja pellonmetsitysten suhteen sekä analysoitiin näiden preferenssien yhteyksiä maisemanhoito-ohjeisiin, vaihtoehtojen ekologiseen terveyteen ja preferenssejä ennustaviin teorioihin. Väitöskirja sisältää neljä tapaustutkimusta, jotka perustuvat hoitovaihtoehtojen visualisointiin ja kyselytutkimuksiin. Tapaustutkimusten pohjalta annetaan ohjeita siitä, kuinka uudistushakkuiden ja pellonmetsitysten visuaalista laatua voidaan parantaa. Väitöskirjan tulokset osoittavat, että uudistamishakkuut voivat herättää myös myönteisiä mielikuvia ja muistoja, jos uudistusala on pieni ja hakkuun välittömät jäljet ovat jo peittyneet. Preferensseihin vaikuttaa hoitovaihtoehdon lisäksi mm. katseluetäisyys, katselupiste ja ympäristö, jossa vaihtoehto on toteutettu. Eri viiteryhmien (metsäammattilaiset, pääkaupunkiseudun asukkaat, ympäristönsuojelijat, tutkimusalueiden matkailijat, paikalliset asukkaat sekä metsänomistajat) maisemapreferenssit olivat hyvin samankaltaisia. Kuitenkin ne ryhmät, jotka saavat ainakin osan elannostaan metsästä - metsänomistajat ja metsäammattilaiset - pitivät metsänhakkuita esittävistä kuvista hieman enemmän kuin muut ryhmät. Nämä tulokset tukevat oletusta, että maisemapreferenssit ovat laajalti yhteneväisiä ainakin yhden kansan tai kulttuurin keskuudessa, vaikka myös viiteryhmä saattaa vaikuttaa preferensseihin jonkin verran. Nykyiset metsämaisemanhoito-ohjeet ovat pitkälti samankaltaisia tässä väitöskirjassa havaittujen maisemapreferenssien kanssa. Myöskään tutkittujen vaihtoehtoisten hoitotapojen ekologisen paremmuuden ja niihin kohdistuvien maisemallisten arvostusten välillä ei ollut suurta ristiriitaa. Kuitenkin joitakin eroavaisuuksia oli; esimerkiksi sekä maisemanhoito-ohjeiden että ekologisten hypoteesien mukaan säästöpuut tulisi jättää ryhmiin, kun taas ihmiset pitivät eniten yksittäin jätetyistä puista. Informaatiomalli ja psyko-evolutionaarinen teoria tarjoavat mahdollisia selityksiä uudistushakkuisiin ja pellonmetsitykseen kohdistuville preferensseille, vaikkakaan tutkimuksen tuloksia ei voida täysin selittää näillä teorioilla

"Guess what I'm doing": Extending legibility to sequential decision tasks

In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoL-MDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against state-of-the-art approaches in several simulated scenarios of different complexity. We also showcase the use of our legible policies as demonstrations for an inverse reinforcement learning agent, establishing their superiority against the commonly used demonstrations based on the optimal policy. Finally, we assess the legibility of our computed policies through a user study where people are asked to infer the goal of a mobile robot following a legible policy by observing its actions

Marketing images and consumers' experiences in selling environments

In a well-functioning market, consumers exert choices not just in purchases of products but also in selections of locations to enjoy shopping. Scholarly research has demonstrated that retail atmospheres impact on shoppers’ pleasurable shopping experiences. Demonstrating the marketing concept in action, shoppers consistently respond to this empowerment by for example, spending more time shopping and spending more money in retail facilities that are perceived to offer a pleasanter atmosphere and experience. This research pivots round an in-depth qualitative study that evaluated the impact of a plasma screens and specific informational content on shopping centre user behaviour. A phenomenological study of the effects of the medium, and the way in which these systems influence behaviour, permitted a far deeper investigation of our sample group vis-àvis increased browsing time and the propensity to spend. A series of eight focus discussions were conducted with local user groups of varying age and gender. Key themes drawn from the group discussions using axial coding indicated that the influence created by the images varied with subjects and settings. The general consensus was that such ‘screens’ created a certain ambience that influenced the way our subjects felt about the selling environment under study. Moreover, for our sample groups, there was clearly a link between the screened images and modern expectations of a selling environment. The plasma screens provided added enjoyment to shoppers’ experiences, providing them with more information enabling more informed shopping choices. The research concludes with implications for strategic marketing, theory and practice

A possibilistic approach to latent structure analysis for symmetric fuzzy data.

In many situations the available amount of data is huge and can be intractable. When the data set is single valued, latent structure models are recognized techniques, which provide a useful compression of the information. This is done by considering a regression model between observed and unobserved (latent) fuzzy variables. In this paper, an extension of latent structure analysis to deal with fuzzy data is proposed. Our extension follows the possibilistic approach, widely used both in the cluster and regression frameworks. In this case, the possibilistic approach involves the formulation of a latent structure analysis for fuzzy data by optimization. Specifically, a non-linear programming problem in which the fuzziness of the model is minimized is introduced. In order to show how our model works, the results of two applications are given.Latent structure analysis, symmetric fuzzy data set, possibilistic approach.