Symbol Emergence in Robotics: A Survey (1509.08973v1)

Abstract: Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.

Symbol Emergence in Robotics: A Survey

The paper "Symbol Emergence in Robotics: A Survey," authored by Tadahiro Taniguchi et al., conducts an extensive survey of the emergent field of Symbol Emergence in Robotics (SER). This field aims to bridge the gap between robotics and the nuanced complexities of human language through the lens of semiotic systems and cognitive development, emphasizing the dynamism of symbol systems that are inherently self-organizing through social and physical interactions.

The research paper identifies the intrinsic challenge in developing autonomous robots capable of engaging in prolonged interactions with humans, particularly through language. Traditional methods focusing on pre-defined and deterministic rule-based communication systems have proved unsatisfactory. Consequently, SER proposes a model where robots learn language through embodied multimodal interactions, thereby dynamically adapting to the human symbol system.

Key Topics and Methodologies

• Multimodal Categorization: An integral part of SER is enabling robots to categorize and form object concepts using multiple sensory modalities—visual, auditory, and haptic—without human supervision. Methods like multimodal latent Dirichlet allocation (MLDA) are employed to form a coherent understanding of the environment from unsupervised data sets.
• Word Discovery and Segmentation: The process by which robots autonomously identify word boundaries in speech—a key stepping stone for language acquisition—is discussed in detail. Techniques like the nested Pitman-Yor LLM (NPYLM) provide statistically robust frameworks for robots to segment words from continuous speech streams despite phonetic ambiguities.
• Double Articulation Analysis: This underlines the layered complexity of linguistic structures, akin to phonemes and words in human language. By unraveling these layers through methods like the sticky hierarchical Dirichlet process-HMM (HDP-HMM), robots can model complex time-series data, enhancing understanding of human motion and behavior as exemplified by driving behaviors.

Implications for Robotics and AI

The implications of SER are profound, both in practical contexts and theoretical advancements. Practically, SER-driven autonomy could lead to robots capable of semantic communication and collaboration with humans in unstructured environments, thus expanding their utility in domestic, industrial, and social scenarios. Theoretically, the paper aligns with embodied cognition perspectives, proposing that comprehension of dynamic human symbol systems could revolutionize AI design principles, enabling machines to navigate complex social dynamics.

Future Directions

The research acknowledges the vast scope for future work. Challenges include improving active perception and learning, enhancing computational semantics, and extending the capabilities of robots to understand and generate human-like language behavior. Another significant future research avenue is developing integrative models that address mutual belief systems and context-aware interactions, further naturalizing human-robot communication.

In conclusion, the survey establishes Symbol Emergence in Robotics as a critical field of paper, pushing boundaries in language acquisition for AI systems and advocating for robots that learn and adapt rather than follow pre-determined instructions. As SER evolves, it may redefine how artificial intelligence perceives and participates in human-robot symbiosis, ultimately leading to machines that not only interpret but become integral parts of the human semiotic landscape.