# Language, Environment, and Robotic Navigation

Johnathan E. Avery
Vivum AI
<EMAIL_ADDRESS>
###### Abstract

This paper explores the integration of linguistic inputs within robotic
navigation systems, drawing upon the symbol interdependency hypothesis to
bridge the divide between symbolic and embodied cognition. It examines
previous work incorporating language and semantics into Neural Network (NN)
and Simultaneous Localization and Mapping (SLAM) approaches, highlighting how
these integrations have advanced the field. By contrasting abstract symbol
manipulation with sensory-motor grounding, we propose a unified framework
where language functions both as an abstract communicative system and as a
grounded representation of perceptual experiences. Our review of cognitive
models of distributional semantics and their application to autonomous agents
underscores the transformative potential of language-integrated systems.

_K_ eywords Distributional Semantics $\cdot$ Symbol Interdependency $\cdot$
Symbol Grounding $\cdot$ Robot Navigation

##  1 Introduction

There is much research exploring the development of autonomous agents capable
of navigating their environments through both direct interaction and
linguistic inputs [1, 2, 3]. This paper aims to survey current robotic
navigation methodologies, with a special emphasis on those integrating
language and semantic information. The primary focus is on Neural Network (NN)
models and Simultaneous Localization and Mapping (SLAM), highlighting their
distinct advantages and potential synergies with linguistic data. Finally, we
discuss cognitive models of distributional semantics that extract map
information from text, and look at ways to incorporate approaches in cognitive
science to autonomous agent applications.

Neural Network approaches leverage deep reinforcement learning algorithms,
allowing robots to dynamically learn from and adapt to their surroundings by
processing extensive sensor data to inform navigation decisions. This method
demonstrates exceptional utility in dynamic environments requiring real-time
responsiveness. In contrast, SLAM enables robots to concurrently generate a
map of their environment and ascertain their location within it, facilitating
navigation in unfamiliar or evolving areas absent pre-existing maps.
Additionally, path planning algorithms, such as A* [4] and Dijkstra’s
algorithm [5], offer methods for calculating the most efficient paths between
points, while behavior-based systems merge simple navigational behaviors, such
as obstacle avoidance and goal seeking, to traverse complex environments.

Our discussion examines current work that integrates language and semantics
into NN and SLAM frameworks. We aim to incorporate cognitive models of
distributional semantics to enable autonomous agents to extract map
information from textual data, bridging the gap between cognitive science
principles and autonomous agent applications. We expect that this cross-
disciplinary approach will enrich the robot’s navigational capabilities and
open avenues for more intuitive human-robot interactions.

###  1.1 Incorporating Language into Robotic Navigation

Simultaneous Localization and Mapping (SLAM) is an approach in robotics that
enables autonomous agents to navigate their environments . Traditional SLAM
focuses on constructing a map of an environment while simultaneously
determining the robot’s location within that map, using sensor data from
cameras, LiDAR, or other devices. This process primarily concerns itself with
geometric and spatial data [6], creating a structural representation of the
environment without necessarily assigning meaning to the objects within it
[7]. As such, traditional SLAM provides the robot with the capability to
navigate but not necessarily to interact with its surroundings in a
contextually meaningful way.

Semantic SLAM (S-SLAM) extends this concept by integrating semantic
understanding into the mapping process [8]. It not only identifies the
locations and shapes of objects but also assigns labels or categories to them,
turning a purely geometric map into a semantic one. This integration relies
heavily on machine learning models, particularly those trained on vast
datasets with labeled examples of objects and their attributes. However, one
limitation of S-SLAM is its dependency on human-annotated data for training
these models, which can be resource-intensive and may not capture the full
complexity and variability of real-world environments. Additionally, while
S-SLAM enables robots to recognize and classify objects, it often still treats
language and semantic information as separate from the core navigational and
operational algorithms, primarily using it to enhance object recognition and
scene understanding [9].

Neural network approaches to robot navigation have evolved to leverage deep
learning for both scene interpretation and deciding on actions. These systems
can process complex sensor inputs to directly inform navigational decisions,
learning optimal paths and behaviors from experience rather than relying
solely on pre-defined maps or models. While powerful, these approaches have
traditionally focused on optimizing for specific tasks or environments, often
without a deep integration of semantic understanding into the representational
space of the agent.

Neural network approaches incorporating language seek to bridge the gap
between linguistic input and an agent’s sensorimotor experience. By training
autonomous agents in simulated environments to locate specific objects
described by various adjectives, researchers establish demonstrate that a link
between linguistic descriptors and the agent’s perceptual experiences can
resolve the symbol grounding problem [10, 11]. Additionally, language has been
used as a command interface, where instructions are provided to the agent,
enabling complex tasks such as manipulating objects with a robotic arm [12,
13] or navigating through simulated environments [14]. These approaches not
only enhance the agent’s ability to perform tasks based on verbal commands,
allowing for more contextually aware robotic navigation.

The proposition of integrating distributional semantic approaches into robot
navigation represents a nuanced shift from these methodologies. Unlike
semantic SLAM, which may rely heavily on predefined labels and human
annotations, distributional semantics offers a way to derive the meaning and
relationships of entities directly from their occurrence and co-occurrence in
data—akin to how language models learn the semantics of words from the
contexts in which they appear. By embedding these principles into neural
network models for navigation, the goal is to enable robots to infer
functional and relational properties of objects and spaces in a more
autonomous, data-driven manner. This would allow robots to navigate their
environment not just in terms of physical layout or labeled categories, but as
a network of meaningfully related concepts and entities.

Such an approach diverges from using language merely as a command interface
for robots; instead, it integrates linguistic and semantic knowledge into the
robot’s decision-making processes. This integration facilitates the robot’s
ability to infer purposes, potential actions, and relationships without
explicit human instruction for each scenario. The goal is to create robotic
agents that can navigate and operate in complex, dynamic environments with a
high degree of complex behavior, leveraging the messiness and richness of
linguistic data to inform their perceptions and actions in the physical world.

##  2 SLAM

SLAM involves two primary tasks: localization (determining the robot’s
position within the map) and mapping (constructing a map of the environment).
These tasks are performed simultaneously based on sensor data from cameras,
LiDAR (Light Detection and Ranging), or other sensing technologies. The SLAM
process typically involves the following steps:

  1. 1.

Data Collection: The robot collects data from its environment using sensors.

  2. 2.

Feature Extraction: It extracts features from the sensor data, which could be
specific points or landmarks in the environment.

  3. 3.

Data Association: The robot identifies which features in the current data
correspond to previously observed features.

  4. 4.

State Estimation: Using the associated data, the robot updates its estimate of
its own position and the positions of features in the environment. This often
involves probabilistic models to account for the uncertainty in sensor data
and feature identification.

  5. 5.

Map Update: The map is updated with the new data, refining the representation
of the environment.

  6. 6.

Loop Closure Detection: The system detects if the robot has returned to a
previously visited area, which can significantly reduce cumulative mapping
errors.

SLAM algorithms can be implemented using various approaches, including
Extended Kalman Filters (EKF), Particle Filters (Monte Carlo methods), and
Graph-Based methods. Each of these approaches deals differently with the
uncertainty inherent in sensor data and motion.

###  2.1 Semantic SLAM

The semantic information in Semantic SLAM systems can be derived from a
combination of sources, and the exact methodology often depends on the
complexity of the system, the specific application, and the available
resources. Here are the primary sources of semantic information:

  1. 1.

Pre-defined Knowledge Graphs and Ontologies: These are structured frameworks
that provide detailed information about various objects, their attributes, and
the relationships between them, facilitating behaviors in complex environments
by providing a rich semantic context [15]. For instance, if a robot recognizes
an object as a chair, the knowledge graph can inform it that a chair is meant
for sitting, and it’s typically found in certain types of rooms.

  2. 2.

Machine Learning Models: Deep learning and other machine learning models are
often trained on large datasets to recognize and classify objects within an
environment. These models can identify objects (like tables, chairs, doors)
and even more abstract concepts (like navigable space) based on visual,
auditory, and tactile data. The semantic labels assigned by these models are
then integrated into the SLAM map, providing a layer of understanding about
what the objects are and potentially their function or purpose.

  3. 3.

Human Input and Interaction: In some systems, semantic information is enriched
or initiated through direct human input or interaction [12]. This can involve
a user labeling parts of the environment or teaching the robot about different
objects and their uses. With sufficient data, the robot can generalize from
these interactions to facilitate navigation of the environment.

  4. 4.

Online Databases and APIs: Some systems might access online databases or use
APIs to fetch real-time information about objects or scenes. For example, a
robot might recognize a specific object but then query an online database for
information regarding its uses, dimensions, or other relevant semantic
information.

  5. 5.

Sensor Data Fusion: Semantic information can also be derived from the fusion
of different types of sensor data, like visual, LiDAR, or radar data [16].
This approach allows the system to exploit the strengths of each sensor type,
leading to more accurate and detailed semantic maps.

In practice, advanced Semantic SLAM systems might use a combination of these
sources to create robust and informative maps that not only navigate the
physical space but also understand the function and context of the environment
and its contents.

####  2.1.1 Ontological Approaches

Ontologies, as formal tools for describing objects, properties, and
relationships within a knowledge domain, offer a structured way to model the
world with which a robotic system might interact. Ontologies serve as
explicit, formal specifications of shared conceptualizations or logical
theories that articulate what a set vocabulary intends to convey [17, 18].
These structured systems enable the categorization and hierarchical
organization of knowledge, making use of classes to represent concepts at
various levels, relations to denote associations between these concepts, and
formal axioms to ensure consistency and coherence among the relationships. By
employing such a structured approach, ontologies facilitate the representation
of the environment in a manner that is both comprehensive and understandable
to machines, typically utilizing languages like first-order logic for formal
specification. This level of organization is crucial in robotics for building
models of the environment where relevant concepts are clearly defined and
interrelated, allowing for efficient navigation, interaction, and task
execution within that environment.

####  2.1.2 Semantic Maps

Semantic maps in the context of robot navigation integrate complex semantic
concepts of the environment, such as the identification of objects, their
functionalities, and the categorization of different spaces based on their
uses and human-like perceptions [19]. These maps go beyond traditional metric
and topological representations by incorporating high-level semantic concepts
that facilitate interaction with humans and improve the robot’s service tasks.
Semantic maps encapsulate valuable environmental information through
associations between objects and their features, elevating the map’s utility
for autonomous navigation and task execution [3]. This approach relies on a
structured understanding of space, where properties such as object presence,
door locations, room shapes, sizes, and appearances are annotated and
associated with sensory data to create a nuanced, multi-layered representation
of the environment [20, 21]. In practice, semantic maps have facilitated robot
navigation of indoor [22] and outdoor [23] environments.

####  2.1.3 S-SLAM vs Our Approach

Our approach, while acknowledging the significance of semantic maps in
enhancing robot-environment interaction, aims to diverge towards a more fluid
understanding of semantic relationships through distributional semantics. This
paradigm shift emphasizes deriving meaning and relationships from the agent’s
interaction with both the physical and linguistic environments, transcending
the more static associations found in traditional semantic maps. Here,
‘semantic’ extends to encompass the dynamic and context-dependent interplay
between concepts, grounded in the robot’s experiences and interactions within
its environment. This method seeks to embed a richer, more adaptable layer of
semantic understanding into the robot’s navigational and operational
framework, aspiring for a more human-like comprehension of environmental
semantics where meanings are not just assigned but discovered and evolved over
time.

Our distributional approach to robot navigation and understanding diverges
significantly from the rigid, axiomatic structures of ontologies [24]. Rather
than pre-defining and categorizing the world into a fixed hierarchy of
concepts and relationships, the distributional approach focuses on learning
from the agent’s continuous interaction with both its physical and linguistic
environments. This approach is underpinned by the symbol interdependency
hypothesis [25], which suggests that the meanings of symbols (or words) emerge
from their use in varied contexts, rather than from their assignment to pre-
defined categories. Distributional semantics, therefore, emphasizes the
derivation of meaning from the statistical properties of symbols as they
appear and co-occur in natural language, allowing an agent to form an
understanding of the world that is fluid and adaptable. This adaptability is
key in dynamic and complex environments where rigid categorizations may fail
to capture the nuanced interplay of objects and actions. By prioritizing
experience and interaction over predefined structures, we expect that the
distributional approach will enable robots to navigate their surroundings in a
way that is more reflective of human cognitive processes, where meanings and
relationships are constantly negotiated and redefined through engagement with
the world.

##  3 Neural Network Approaches

Neural network (NN) models represent a shift from traditional SLAM algorithms
in robot navigation, emphasizing learning from data over probabilistic
modeling and geometric analysis. Unlike SLAM’s reliance on explicit models for
localization and mapping, NNs excel in deriving complex patterns and
relationships directly from data. This distinction underscores NNs’
versatility in adapting to diverse environments without being constrained by
the need to model uncertainty or geometry explicitly as in SLAM.

NN approaches introduce a dynamic and flexible framework for robot navigation.
For example, Convolutional Neural Networks (CNNs) enhance feature extraction
capabilities, enabling more nuanced scene interpretation, and Recurrent Neural
Networks (RNNs) facilitate sequence data processing, improving localization
and mapping efforts. This integration of NNs into the SLAM process signifies
the potential for a symbiotic relationship where NNs augment SLAM’s geometric
precision with adaptive, data-driven insights.

The integration of language into NN robotic navigation is distinct from SLAM,
particularly in how they incorporate language for navigational tasks. NN
models utilize language in various innovative ways, from serving as an oracle
in reinforcement learning scenarios where linguistic cues guide reward
mechanisms [26], to processing complex commands for direct action [14].
Integrating language into NN approaches enable agents not only navigate but
also interpret and act within their surroundings in a contextually rich manner
[10].

###  3.1 Language as Command Interface

Integrating language into robot or simulated agent behavior growing field.
Commonly, language is implemented as a command interface as a means to guide
and direct robot behavior [27]. This approach seeks to bridge the
communication gap between humans and robots, making interactions more
intuitive and accessible. Instead of relying on pre-programmed instructions or
complex programming languages, robots can interpret verbal or written
instructions, translating the complexities of human language into actionable
tasks.

Numerous approaches have successfully demonstrated that incorporating language
as the command interface for autonomous agents improves task completion in
unfamiliar environments in imitation learning [28], meta-reinforcement
learning [13], and inverse-reinforcement learning [14].

Goyal et al. introduce PixL2R, a model that innovatively uses natural language
descriptions to map visual inputs directly to rewards in reinforcement
learning environments, thereby facilitating policy learning without predefined
reward structures [29]. This approach enhances the agent’s exploration
efficiency by generating intermediate rewards based on the alignment between
executed trajectories and linguistic task descriptions, a method proving to
significantly increase sample efficiency across various tasks. PixL2R
demonstrates the potential of natural language to not only direct but also
accelerate the learning process, challenging traditional, hand-designed reward
systems with its adaptability and effectiveness in both sparse and dense
reward scenarios. Moreover findings by [14] suggest that using language-
conditioned rewards offer a more flexible and transferable approach to task
specification in reinforcement learning, outperforming direct language-
conditioned policies in terms of generalization across new scenarios.

Language as a command interface facilitates robot behavior and navigation by
translating human verbal or written instructions into actionable tasks,
allowing robots to understand and execute complex commands in their operating
environments. This approach leverages natural language processing to bridge
the gap between human communication and robotic actions, making interactions
more intuitive and expanding the robots’ capabilities to navigate and perform
tasks with minimal human intervention.

###  3.2 From Command Interface to Representational Space

Building on the foundation of language as a command interface, we propose
evolving this concept towards language as a representational space within
robotic systems. This expanded paradigm aims not only for robots to understand
and execute commands but also to enable them to produce meaningful language in
response to their environments. This approach envisions a more integrated and
reciprocal relationship between language and sensory experiences, allowing
robots to not only act upon language commands but also to describe, reason,
and learn from their environmental interactions through language.

In this context, language may serve as a medium for bidirectional
communication, enhancing the interaction between humans and robots beyond
simple command execution. By treating language as a communication interface,
the focus shifts towards facilitating a two-way dialogue where robots can
provide feedback, ask for clarification, and share observations, making the
interaction more dynamic and collaborative. This is a significant departure
from the narrow interaction paradigm of language as a mere command interface,
which traditionally directs communication from the human user to the agent in
a unidirectional manner.

Furthermore, conceptualizing language as a representational space within
robotic systems allows for a more profound integration of linguistic and modal
experiences. In this integrated framework, language processing and sensory
processing are not isolated; instead, they inform and enhance each other. Such
a system enables robots to not only process and respond to linguistic and
sensory inputs concurrently but also to use language to fill gaps in their
knowledge and to interpolate between linguistic and sensory experiences in a
meaningful way. This symbol interdependency between language and modal
experience enables robots to navigate and interact with their environment in a
more nuanced and human-like manner, paving the way for more sophisticated and
autonomous robotic agents capable of complex reasoning and learning.

##  4 Maps, Language, and Cognition

###  4.1 Learning Semantic Maps From Natural Language

Cognitive maps highlight the usefulness of language, emphasizing the
redundancy between our use of words in contexts and their physical
environmental counterparts.

The incorporation of language into robotic navigation systems has primarily
focused on addressing the symbol grounding problem [10, 30, 11], or on
implementing language as a command interface for agents. Alternatively,
research has shown that language serves as a substrate to communicate features
of a map effectively. On a local level, one can convey information about a
location with a single statement (e.g., "Your cup is on the left"). On a
larger scale, language patterns robustly support map features, such as city
distributions. Research using Latent Semantic Analysis (LSA), an early model
of distributional semantics based on word co-occurrences, demonstrated that
word representations for cities accurately reproduced a map of the United
States [31]. A follow-up study used the "Lord of the Rings" trilogy as a text
corpus [32], showing that LSA could reliably reconstruct a map of Middle Earth
purely from the text. Furthermore, the maps derived from LSA closely matched
those created by human participants tasked with the same activity, suggesting
a similarity in cognitive map construction processes between humans and the
distributional semantic algorithm. Cognitive maps highlight the usefulness of
language, emphasizing the redundancy between our use of words in contexts and
their physical environmental counterparts.

Follow-up research pinpointed the specific statistical distributions
facilitating the operation of LSA and other distributional semantic algorithms
[33]. Even when the map was not fully articulated by the corpus, the map was
reliably reconstructed, showcasing the effectiveness of language in encoding
relationships even with a high sparsity of references. Follow up
demonstrations with transformers showed that the embedding space alone could
usefully reconstruct maps, and that the downstream attention and feed-forward
computations enabled the model to produce accurate and relevant utterances
[34].

###  4.2 Symbol Interdependency

The distinction between amodal, symbolic cognition, and grounded, embodied
cognition is a cornerstone concept in cognitive science and artificial
intelligence [35]. Here’s an articulation of the various sides of the debate.

####  4.2.1 Symbolic cognition entails the manipulation of amodal symbols

Symbolic cognition revolves around the idea that human thought processes can
be emulated through the manipulation of symbols that stand in for objects,
concepts, and ideas in the real world. These symbols are "amodal" in that they
do not inherently possess sensory attributes such as sight, sound, or touch.
Instead, they function through an abstract representation, allowing for
complex thought processes, reasoning, and problem-solving in a manner that is
decoupled from the physical characteristics of the environment.

####  4.2.2 Symbolic cognition works because the relationships between
concepts is preserved, despite the concepts themselves not being grounded

The power of symbolic cognition lies in its ability to preserve the web of
relationships between concepts. This preservation enables the manipulation and
examination of these relationships independently of the physical or sensory
characteristics of the objects or concepts they represent. It’s this
abstraction that allows for high-level reasoning and abstract thought, key
aspects of human intelligence.

####  4.2.3 Grounded cognition entails the relationships of physical or modal
environments

Grounded cognition contrasts with symbolic cognition by anchoring concepts and
their relationships within the sensory and physical experiences of the world.
Here, knowledge and thought are directly tied to sensory inputs and motor
outputs, suggesting that understanding stems from embodied experiences.
Grounded cognition posits that concepts are understood in terms of their
relationships to physical or modal environments, which means cognition is
heavily influenced by an individual’s sensory and motor interactions with
their surroundings.

###  4.3 Reconciling Symbolic and Embodied Cognition: Insights and Challenges

Symbolic cognition offers a framework for understanding and emulating human
intelligence through the manipulation of abstract symbols and their
interrelationships, without the need for these symbols to be directly grounded
in sensory experiences. This contrasts with grounded cognition, which
emphasizes the importance of sensory and motor experiences in shaping
understanding and thought processes. Both approaches offer valuable insights
into the nature of cognition, suggesting that human thought may encompass
elements of both symbolic manipulation and sensory-motor grounding.

From the perspective of embodied cognition, the major drawbacks to symbolic
cognition center on its disconnection from sensory and motor experiences,
which are fundamental to cognition. Embodied cognition critiques argue that
without symbol grounding, where words and concepts are tied to bodily actions
and sensory experiences in the environment, symbolic cognition remains an
endless loop of defining symbols with other symbols, akin to a "symbolic
merry-go-round" [36]. This critique posits that comprehension cannot be
achieved through mere manipulation of amodal symbols, as it overlooks the
crucial role of perceptual experiences and motor actions in understanding
language.

Importantly, experimental evidence supports the embodied cognition view by
demonstrating that comprehension involves the reenactment of sensory and motor
experiences. For example, neurophysiological studies (e.g., [37]) have shown
that processing action words activates brain areas associated with the
corresponding motor actions, indicating a direct link between linguistic
stimuli and sensorimotor processes. Comprehension of language not only
involves linguistic regularities but also relies heavily on the activation of
perceptual systems.

Embodied cognition argues that the abstract and arbitrary nature of linguistic
symbols, as treated in symbolic cognition, cannot form the basis of meaning on
their own. Instead, meaning arises from the interaction of these linguistic
symbols with embodied representations, suggesting that language comprehension
and conceptual processing are intrinsically linked to sensory and motor
experiences. This perspective challenges the efficacy of purely symbolic
models and emphasizes the importance of grounding linguistic symbols in
physical and modal experiences to achieve genuine comprehension and meaningful
interaction with the environment.

###  4.4 Bridging Symbolic and Embodied Cognition for Robotic Navigation

The symbol interdependency hypothesis [25] proposes a synthesis where symbolic
and embodied cognition are not seen as mutually exclusive but rather as
complementary and mutually reinforcing. This perspective acknowledges that
while symbolic cognition operates through the interdependencies among amodal
linguistic symbols, embodied cognition grounds these symbols in perceptual and
sensory experiences. Symbol interdependency suggests that language
comprehension involves both symbolic processes, through the relationships
among abstract symbols, and embodied processes, through the references these
symbols make to tangible, perceptual representations.

This hypothesis highlights that language naturally evolves to encode
relationships that exist in the world, including those grounded in physical
experiences, thereby serving as a communicative shortcut that encapsulates
both symbolic and perceptual information. This dual nature of language,
encoding both amodal symbolic patterns and modal, perceptual information,
offers a powerful framework for understanding how language can encode
information about the world, such as action dynamics and object
characteristics, in a way that is readily accessible and easily communicated.

For integrating language into robotic navigation, the symbol interdependency
hypothesis offers a foundational basis for developing systems that can
interpret and use language in a way that reflects both its symbolic and
embodied aspects [38]. By recognizing that language can encode perceptual
information, robotic systems can be designed to leverage linguistic data to
enhance spatial understanding and navigation tasks [39]. Such systems could
utilize linguistic input to supplement sensor data, enriching the robot’s
model of the environment with information that is implicitly encoded in
language, such as spatial relations and object attributes. We expect that this
approach will not only enhance the robot’s ability to navigate through its
environment but also facilitates more natural and intuitive human-robot
interaction, bridging the gap between high-level linguistic communication and
low-level sensory-motor tasks.

##  5 Integrating Symbol Interdependency and Robotic Navigation

In our exploration of the symbol interdependency hypothesis, we anticipate
implementing and investigating its principles through the lens of autonomous
agents navigating a linguistically enriched and modal environment. At the
heart of our approach lies the integration of abstract linguistic inputs with
the tangible sensory data of simulated 3D landscapes, aimed at embodying the
symbiotic essence of symbolic and embodied cognition. By designing agents with
the capability to process both visual and linguistic stimuli, underscored by
convergence zones for seamless integration, we apply our understanding of
human cognitive processing within a structured model. We foresee that engaging
with linguistic environments will not only bolster the agents’ modal
navigational abilities but also foster a bidirectional reinforcement between
linguistic learning and sensory experience. Moreover, we posit that immersion
in the modal environment should suffice for agents to articulate precise
linguistic descriptions independently, grounded in their direct and
descriptive environmental learning. We aim to illuminate the practical
applicability of the symbol interdependency hypothesis, bridging theoretical
cognition with the tangible implementations of linguistic input and sensory
experience in the domain of autonomous navigation systems.

###  5.1 Agent and Environment

Our investigation of the symbol interdependency hypothesis is aimed at the
interplay between linguistic and modal environments within the framework of
autonomous agents. This exploration delineates two environmental inputs that
an agent encounters: the linguistic environment and the and the modal
environment. The linguistic environment encompasses language inputs provided
to the agent and subsequent language the agent generates. The modal
environment is defined as a simulated 3D space, likely a series of landmarks
in order to align with ongoing research into linguistic cognitive maps (rather
than room-to-room navigation as is conventional in robot navigation
literature).

At the core of our architectural design for such agents is the processing
systems of both visual (or scene-related) stimuli and linguistic data. This
dual-input system is supported by several key modules, which, though not
specified in detail within this paper, are essential for processing both
visual scenes and linguistic information.

Importantly, the agent architecture will include convergence zones—dedicated
areas within the agent’s framework that integrate both linguistic and visual
inputs. We expect that these zones are instrumental in facilitating a seamless
interaction between the scene and language processing modules. We acknowledge
the complexities and limitations inherent in simulating human cognition.
Recognizing that human cognition is not entirely modular, we note that certain
brain regions have stable roles in processing specific information types.

Our agents will employ an encoder-decoder setup where the encoder provides
context for the decoder, allowing it to process previously produced tokens and
generate the most appropriate subsequent token. This setup reflects findings
which illuminate the insufficiency of sparse rewards in reinforcement learning
tasks for achieving language grounding—the crucial connection between
linguistic symbols and their corresponding visual stimuli [10].

Through this structured exploration, we aim to contribute to the understanding
of how autonomous agents can effectively navigate and interact within their
environments by leveraging the symbiotic relationship between linguistic and
modal inputs. This approach not only seeks to enhance the agents’ navigational
abilities but also underscores the potential for more nuanced and contextually
rich human-robot interactions, bridging the gap between high-level linguistic
communication and sensory-motor functionalities.

###  5.2 Testing Symbol Interdependency

Given the symbol interdependency hypothesis, we anticipate three outcomes in
our agents. First, we would expect that experience with the linguistic
environment should facilitate the agent’s ability to navigate the modal
environment. Second, we would expect that learning in both the linguistic and
modal domains should be mutually reinforcing. Third, we would expect that
learning in the modal environment should be sufficient to enable an agent to
produce accurate linguistic statements without direct linguistic training.

  1. 1.

Learning Language from a Corpus: We would expect agents that are trained on a
corpus that describes an environment to learn to navigate the environment more
efficiently than an agent with no prior training on corpus statistics.

  2. 2.

Learning Language/Distributional Information from Environmental Interaction:
This involves agents generating something akin to an internal monologue based
on their visual experiences, assuming a pre-existing mapping between visual
stimuli and word labels. The sequence of visual labels in the agent’s
monologue is expected to reflect the physical arrangement of objects in its
environment. We would expect an agent that learns semantic representations
from their experiences should learn to navigate its environment more
effectively than the same agent that does not learn semantic representations
based on interaction with its environment.

  3. 3.

Communicating Representations Learned from Environmental Interaction: Under
the symbol interdependency hypothesis, we envision overlapping sets of symbols
and modal experiences, where some symbols are directly grounded in modal
interactions, yet some modal experiences lack direct linguistic expression. We
would expect that an agent exposed to both linguistic and modal environments
is expected to produce more accurate utterances than agents exposed to a
singular (even if more complete) environment, benefiting from the integration
of information across domains.

These points aim to articulate the expected cascade of information between
modal and symbolic processing, testing the validity of the symbol
interdependency hypothesis in bridging linguistic and sensory experiences
within cognitive models.

##  6 Conclusion

We aim to advance the integration of linguistic inputs into the navigational
capabilities of autonomous agents, guided by the symbol interdependency
hypothesis. Our approach delineates the distinct but interconnected roles of
linguistic and modal environments in shaping cognitive models for robotic
navigation.

Looking ahead, we intend to implement and test the outlined architecture,
focusing on the impact of integrating linguistic and visual inputs within the
agents. Key to our future direction is the examination of how agents can
leverage linguistic data to enhance spatial understanding and decision-making
processes, thereby enriching their interaction with the environment.

The significance of this work extends beyond the immediate advancements in
robotic navigation; it contributes to the broader discourse on cognitive
models, offering insights into the complex interplay between language and
sensory experiences. By grounding our investigation in the symbol
interdependency hypothesis, we underscore the potential for linguistic data to
transform the way autonomous systems navigate and interpret their
environments. This research paves the way for more intuitive and natural
human-robot interactions, marking a significant step towards the realization
of truly autonomous agents capable of navigating the world with the same
nuance and understanding as humans.

## References

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