Recursive Spoken Instruction-Based One-Shot Object and Action Learning

Scheutz, Matthias; Krause, Evan; Oosterveld, Bradley; Frasca, Tyler M.; Platt, Robert

doi:10.24963/ijcai.2018/752

Cited by 26 publications

(22 citation statements)

References 7 publications

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“…Another complexity is adjudicating between conflicting norms or goals, which might be addressed by integrating existing approaches to the problem (Kasenberg and Scheutz 2018b; Vasconcelos, Kollingbaum, and Norman 2009). To increase the system's scope beyond a basic set of actions, predicates and heuristics, the system could employ one-shot learning of actions and objects , multi-modal semantic grounding (Thomason et al 2016), and automatic feature selection (Abe 2010). The sources of normative information could also be expanded, allowing for the inference of norms from how agents interact with objects and other agents in the environment.…”

Section: Discussionmentioning

confidence: 99%

That’s Mine! Learning Ownership Relations and Norms for Robots

Tan

Brawer

Scassellati

2019

AAAI

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The ability for autonomous agents to learn and conform to human norms is crucial for their safety and effectiveness in social environments. While recent work has led to frameworks for the representation and inference of simple social rules, research into norm learning remains at an exploratory stage. Here, we present a robotic system capable of representing, learning, and inferring ownership relations and norms. Ownership is represented as a graph of probabilistic relations between objects and their owners, along with a database of predicate-based norms that constrain the actions permissible on owned objects. To learn these norms and relations, our system integrates (i) a novel incremental norm learning algorithm capable of both one-shot learning and induction from specific examples, (ii) Bayesian inference of ownership relations in response to apparent rule violations, and (iii) perceptbased prediction of an object's likely owners. Through a series of simulated and real-world experiments, we demonstrate the competence and flexibility of the system in performing object manipulation tasks that require a variety of norms to be followed, laying the groundwork for future research into the acquisition and application of social norms.

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Section: Discussionmentioning

confidence: 99%

That’s Mine! Learning Ownership Relations and Norms for Robots

Tan

Brawer

Scassellati

2019

AAAI

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“…In this case, the best way to leverage findings from lab speech is as inspiration, guiding feature extraction and pointing to directions of meaningful variation that can be verified in a larger datasets that are closer to the material the application will be interacting with. However, in the last few years there has been an uptick in "oneshot learning" [18,19,20]. The goal of this research direction is to adapt a system trained on a large amount of data to model a new or related phenomenon with very little data.…”

Section: Challenge #2: Bridge the Gap Between Experimental Data And Bmentioning

confidence: 99%

Speech, Prosody, and Machines: Nine Challenges for Prosody Research

Rosenberg¹

2018

Speech Prosody 2018

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Speech technology is becoming commonplace. Traditional telephony based interactive voice systems have been joined by virtual assistants and navigation systems to create a broad ecosystem of voice enabled technologies. Prosody is an essential component to human communication, but machines still lag in their ability to understand information communicated prosodically and to produce human-like intonation. This paper poses nine challenges designed to effectively and more thoroughly integrate prosody into current speech technologies. These include long-standing and contemporary concerns surrounding the availability and utility of data, gaps in linguistic theory and specific technological issues. Each of these challenges have received some attention, additional work is necessary to bring the role of prosody in speech technology closer to its role in human communication.

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“…To our knowledge very few works are tackling it from this angle. In [13] a cognitive architecture is developed to allow the learning of objects and actions. While in [14] the teaching process also uses semantic information, the design of the teaching process is different and we tried to adopt a communication model as close as possible from what the end user could expect.…”

Section: Related Workmentioning

confidence: 99%

Human-Robot Interactive Learning Architecture using Ontologies and Symbol Manipulation

Angleraud

Houbre

Kyrki

et al. 2018

2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Robotic systems developed for support can provide assistance in various ways. However, regardless of the service provided, the quality of user interaction is key to adoption by the general public. Simple communication difficulties, such as terminological differences, can make or break the acceptance of robots. In this work we take into account these difficulties in communication between a human and a robot. We propose a system that allows to handle unknown concepts through symbol manipulation based on natural language interactions. In addition, ontologies are used as a convenient way to store the knowledge and reason about it. To demonstrate the use of our system, two scenarios are described and tested with a Care-O-Bot 4. The experiments show that confusions and difficulties in communication can effectively be resolved through symbol manipulation.

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Recursive Spoken Instruction-Based One-Shot Object and Action Learning

Cited by 26 publications

References 7 publications

That’s Mine! Learning Ownership Relations and Norms for Robots

That’s Mine! Learning Ownership Relations and Norms for Robots

Speech, Prosody, and Machines: Nine Challenges for Prosody Research

Human-Robot Interactive Learning Architecture using Ontologies and Symbol Manipulation

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