Cog-GA: A Large Language Models-based Generative Agent for Vision-Language Navigation in Continuous Environments
Zhiyuan Li Yanfeng Lu Yao Mu Hong Qiao




A background-estimation technique for the detection of extended gamma-ray structures with IACTs
     T. Wach 1 A. Mitchell 1 L. Mohrmann 2
    September 9, 2024
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§ ABSTRACT 

Vision Language Navigation in Continuous Environments (VLN-CE) represents a frontier in embodied AI, demanding agents to navigate freely in unbounded 3D spaces solely guided by natural language instructions. This task introduces distinct challenges in multimodal comprehension, spatial reasoning, and decision-making. To address these challenges, we introduce Cog-GA, a generative agent founded on large language models (LLMs) tailored for VLN-CE tasks.

Cog-GA employs a dual-pronged strategy to emulate human-like cognitive processes. Firstly, it constructs a cognitive map, integrating temporal, spatial, and semantic elements, thereby facilitating the development of spatial memory within LLMs. Secondly, Cog-GA employs a predictive mechanism for waypoints, strategically optimizing the exploration trajectory to maximize navigational efficiency.

Each waypoint is accompanied by a dual-channel scene description, categorizing environmental cues into 'what' and 'where' streams as the brain. This segregation enhances the agent's attentional focus, enabling it to discern pertinent spatial information for navigation. A reflective mechanism complements these strategies by capturing feedback from prior navigation experiences, facilitating continual learning and adaptive replanning.

Extensive evaluations conducted on VLN-CE benchmarks validate Cog-GA's state-of-the-art performance and ability to simulate human-like navigation behaviors. This research significantly contributes to the development of strategic and interpretable VLN-CE agents.







§ INSTRUCTION

Vision Language Navigation (VLN) plays a pivotal role in robotics, where an embodied agent carries out natural language instructions inside real 3D environments based on visual observations. Traditionally, the movements of agents in VLN environments are processed by a pre-prepared navigation graph that the agent traverses. Recognizing this, Krantz et al.<cit.> introduced an alternative approach known as Vision-Language Navigation in Continuous Environments (VLN-CE). Unlike traditional methods, VLN-CE eliminates the need for navigation graphs, enabling agents to move freely in 3D spaces. This framework has gained prominence for its realistic and adaptable approach to robotic navigation, allowing agents to respond effectively to verbal commands. Previous works such as ERG<cit.>, VLN-Bridge<cit.>, and CKR model<cit.> primarily focus on reinforcement learning methods. However, reinforcement learning requires lots of interactive data.


Large language models (LLMs) have recently illustrated remarkable performance in various fields. Several recent studies have explored the versatility of LLMs in interpreting and navigating complex digital environments, demonstrating their remarkable performance in various fields. For instance, Velma<cit.> adopts the LLM in Street View VLN tasks. Esc<cit.> and LFG<cit.> focus on zero-shot object navigation(ZSON) tasks. ProbES<cit.> further enhances the generalization of LLMs in REVERIE tasks. We aim to leverage the wealth of prior knowledge stored in LLMs to construct an agent with better generalization abilities for VLN-CE tasks. This agent receives dual input from visual and language modalities. It summarizes the key information from the two modalities through its abstract knowledge structures powered by LLMs, bridging sensory modalities and establishing abstract concepts and knowledge structures. 

To this end, we propose Cog-GA (Cognitive-Generative Agent), a LLM-based generative agent for 
vision-language navigation in continuous environments. One of the key challenges in building an efficient VLN agent with LLMs is their lack of inherent spatial memory abilities, as LLMs are trained on flattened text input, lacking the ability to model 3D spatial environments natively. To address this, we introduce the cognitive map, which maintains spatial information related to scene descriptions and landmark objects at each navigation step as a graph. These recorded spatial memories are then retrieved and utilized in subsequent navigation steps. Another core challenge is that valuable waypoints for decision-making by LLMs are often sparsely distributed in the environment. To construct a more reasonable and efficient search space for the agent, we employ the waypoints predictor<cit.>. For each waypoint, we adopt the dual-channel theory<cit.> to describe the observed scene efficiently, which divides scene descriptions into the "what" stream related to landmark objects and the "where" stream concerning spatial characteristics of indoor environments. This division aligns well with the navigation task of reaching objects in different environmental contexts. Since the instructions received by the agent can be separated into sub-instructions corresponding to reaching objects and switching environments, the LLM can effectively focus on the current target. We further introduce a reflection mechanism with a waypoint instruction method to enable the agent to abstract new knowledge from interactions with the environment. 

The LLM then combines these past experiences with the spatial information from the cognitive map to perform more informed navigation planning and facilitate continuous learning and adaptation. Cog-GA employs the LLM to fuse perception results and historical information by maintaining temporal, spatial, and descriptive memories in a cognitive map. 
Each navigation step optimizes the search space using predicted waypoints, abstracting scene descriptions through dual "what" and "where" channels to emphasize relevant objects and spatial contexts. The system learns from experience and adapts its policy, with a reflection mechanism capturing navigation feedback via the LLM.




Extensive experiments on the VLN-CE dataset confirm that our Cog-GA agent achieves promising performance with psychologically human-like behavioral simulation. This work lays a foundation for developing more intelligent, human-like vision-language navigation agents that can strategically adapt to new environments while leveraging prior knowledge from language models. Our key contributions can be summarized as follows:

 
    
  * We propose the Cog-GA framework, a generative agent based on large language models (LLMs) for vision-language navigation in continuous environments (VLN-CE), simulating human-like cognitive processes, including cognitive map construction, memory retrieval, and navigation reflection. Experiments demonstrate that Cog-GA achieves a 48% success rate comparable to the state-of-the-art on the VLN-CE dataset.

    
  * We introduce a cognitive map-based memory stream mechanism that stores spatial, temporal, and semantic information, providing contextual knowledge to the LLM to facilitate navigation planning and decision-making.

    
  * We introduce a waypoints predictor and a dual-channel ("what" and "where") scene description approach that optimizes the search space, enabling the LLM to focus on current goals. This method significantly improves the navigation success rate.
 




§ RELATED WORK





 §.§ Vision-Language Navigation


Visual language navigation (VLN) is an emerging interdisciplinary field that has garnered significant attention from natural language processing, computer vision, and robotics communities. Anderson et al. first proposed the VLN task in 2018, introducing the Room-to-Room (R2R) dataset based on real-world environments <cit.>. Following the release of the R2R dataset, various expansions of VLN tasks emerged, such as the outdoor visual language navigation Touchdown <cit.> and Remote Embodied Visual Referring Expression in Real Indoor Environments (REVERIE) <cit.>. Numerous methods have been proposed to address these tasks. A notable advancement is the Reinforced Cross-Modal Matching (RCM) approach, which has outperformed baseline methods by 10% 

The History Aware Multimodal Transformer (HAMT) has set a new benchmark in long-term navigation, demonstrating the importance of incorporating historical context in navigation tasks <cit.>. 



 §.§ VLN in Continuous Environments


In traditional VLN tasks, agents navigate through a restricted graph, an unrealistic assumption for real-world navigation robots. Krantz et al. <cit.> extended the discrete R2R VLN task setup to continuous environments, where agents make navigation decisions in freely traversable 3D spaces. The VLN-CE framework introduces new challenges and brings the task closer to real-world navigation scenarios. Zhang et al. <cit.> highlighted the significant impact of visual appearance features on agent performance, underscoring the need for models that generalize better across diverse environments. Similarly, Wang et al. <cit.> introduced the Reinforced Cross-Modal Matching (RCM) approach, achieving notable improvements in navigation performance. Guhur et al. <cit.> demonstrated that in-domain pretraining using the BnB1 dataset significantly enhances generalization to unseen environments.

To address the complexities of the VLN-CE task, Hong et al. <cit.> showed that agents navigating with predicted waypoints perform significantly better, reducing the discrete-to-continuous gap. Wang et al. <cit.> proposed an Environment Representation Graph (ERG) that strengthens the relationship between language and environment, leading to improved performance in VLN-CE tasks. Chen et al. <cit.> introduced DNA, a direction-guided navigator agent that integrates directional cues from instructions into the encoder-decoder framework. However, current VLN-CE methods require massive training to obtain prior knowledge.



 §.§ Large Language Models Guided Navigation


Leveraging their powerful information processing capabilities and extensive prior knowledge, large language models (LLMs) have emerged as a novel approach for reasoning in navigation tasks. The VELMA model <cit.> uses LLMs to predict movement directions based on landmark information from language instructions. The Esc model <cit.> considers correlations with target objects at both the object and room levels. For zero-shot object navigation, LFG <cit.> introduced chain-of-thought (CoT) reasoning in LLMs to avoid navigating to irrelevant areas. Cai et al. <cit.> extended the use of CoT by clustering panoramic images into scene nodes and employing CoT to decide whether to explore or exploit, selecting images most likely to contain the target object and navigating accordingly. The Prompt-based Environmental Self-exploration (ProbES) <cit.> significantly enhances LLMs' generalization capabilities in tasks like VLN and REVERIE, indicating a shift towards more adaptive and context-aware LLMs in navigation. Co-NavGPT <cit.> applies LLMs' decision-making processes in multi-robot collaborative navigation, centrally planning midterm goals for each robot based on online map information. However, more cognitive processes in the brain can also be introduced to this process.



§ MATERIAL AND METHODS


We leverage the LLM to stimulate the cognitive process of navigation, including creating the cognitive map, instruction understanding, and the reflection mechanism. By introducing LLM, the VLN agent can obtain tremendous prior knowledge, which enables the agent to process tasks effectively. We construct a graph-based cognitive map as external memory to address the LLM lack of long-term and spatial memory. That allows the LLM-based agent to understand and remember the continuous environment.




 §.§ Generative Agent for VLN-CE Tasks





We divide VLN-CE tasks into three phases: generating the search space, high-level target planning, and low-level motion generation. Constructing the search space, a crucial preprocessing step, segments the continuous environment into waypoints, simplifying navigation to point selection and improving efficiency. We use a planner based on large language models (LLMs) for high-level target planning. It selects a waypoint as the next target based on current sub-instructions using spatial memories from the cognitive map of the memory stream. The target waypoint is then sent to the motion generator for action execution.

We propose a generative agent for VLN-CE tasks, including the waypoint predictor, memory stream, instruction processing, high-level planner, and reflection module. The instruction processing module divides the task into shorter-range sub-instructions. The waypoint predictor uses panoramic observations to construct the waypoint search space at each step. A scene describer characterizes the observed scene, dividing descriptions into the "what" stream (landmark objects) and the "where" stream (spatial characteristics). These descriptions adjust the sub-instruction for alignment with the environment, allowing the planner to focus on the current target: cognitive maps and reflection memories from the memory stream guide the high-level planner. The planner uses scene descriptions, memories, and the sub-instruction to compose a prompt for the LLM. The target waypoint index is conveyed to the low-level actuator. During movement execution, a reflection generator assesses the navigation results, constructing feedback to evaluate each navigation impact of step.





 






































 §.§ Cognitive Map based Target Inference

Humans and animals create cognitive maps to code, store, and retrieve information about their environments' relative locations and attributes. Introduced by Edward Tolman in 1948 <cit.>, this concept explains how rats learn maze layouts and apply them to humans for navigation and spatial awareness. We use the cognitive map for LLM-based agents and VLN-CE tasks.

A significant challenge for LLMs is the lack of long-term and spatial memory, making external memory crucial <cit.>. We address this by introducing a graph-based cognitive map as external memory, which builds and stores spatial memory to help the LLM understand and remember the environment.

The cognitive map starts as an undirected graph 𝒢(ℰ, 𝒩) with nodes 𝒩_p for traversed spaces and 𝒩_o for observed objects. 𝒩_o nodes connect to their corresponding 𝒩_p nodes with 1-weight edges ℰ_p,o. Connections between 𝒩_p nodes (ℰ_p) are weighted to represent distance and angle between waypoints, ranging from 0.25 to 3 for distance and 1 to 8 for direction. Each 𝒩_p node also has a time step label t. The cognitive map graph is represented as:

    𝒢({ℰ_p,o, ℰ_p}, {𝒩_p, 𝒩_o})


Retrieving the cognitive map is crucial for target inference. As shown in Figure <ref>, we define two retrieval methods: the history and observation chains. The history chain focuses primarily on navigated nodes, providing planners an abstract view of the current path. In contrast, the observation chain focuses on potential targets between the current and previous positions, offering a broader view of past decisions.



Figure <ref> outlines the target inference process. After the waypoint predictor segments the panorama, the LLama-based scene describer processes the waypoint image into 'where' and 'what' related words. These waypoints update the cognitive map in the memory stream. The history chain, environment descriptions, reflection memory, and sub-instruction form a unified prompt input to the LLM-based planner. The planner outputs the target waypoint index, which is stored in the memory stream for the cognitive map. The actuator then extracts distance and angle information for the agent's action.





 §.§ Instruction Rationalization based Instruction Processor


For VLN-CE agents, handling instructions is nontrivial. Using unprocessed instructions directly confuses the planner, causing it to perform meaningless actions. To solve this, we propose an instruction rationalization mechanism. We break the instruction into several sub-instructions using LLMs to guide the agent. However, the original sub-instructions often lack context. For example, the sub-instruction "Exit the living room." might confuse the agent about its current target, causing it to repeat routes. Therefore, we include current environment information and the unprocessed instruction to adjust the sub-instruction. 
This process can be expressed as 

    I_i,1 = R(I_i,0|D, ℐ) → ... → I_i,n = R(I_i,n-1|D, ℐ)

where I_i,0 is the original sub-instruction, and ℐ is the unprocessed instruction. 

As the agent moves through the environment, sub-instructions are continuously rationalized. If a sub-instruction is completed, the agent moves to the next one until all sub-instructions are finished. For example, the rationalized sub-instruction "Find the door of the living room and look for the sign to the kitchen" is more effective for the agent. At the start of the navigation task, the agent breaks down the natural language instruction into multiple sub-instructions. Each sub-instruction is updated based on observations at each time step as the agent moves, a process we call instruction rationalization.
Detailed discussions of instruction rationalization will be provided in the appendix.



 §.§ Generative Agent with Reflection Mechanism




The concept of a generative agent, which blends AI with human-like simulation, represents a significant advancement. These agents mimic human behaviors based on interactions with the environment and past experiences. VLN-CE closely mimics real-world navigation, making it an ideal application for simulating psychological processes during human navigation.



While navigating, the agent receives panoramic waypoint inputs as observations. The scene describer provides a structural description of each waypoint to the planner, along with the history chain from the cognitive map (section <ref>) and reflection memories from the memory stream. The planner uses this information to determine the next navigation waypoint based on the sub-instruction.


After the planner identifies the target, the angle and distance to the waypoint are sent to the low-level actuator to move the agent. The agent then reflects on its movements to gather valuable experiences for future tasks. This reflection helps the agent understand why it succeeded or failed, gaining general knowledge about the environment. However, LLMs can be overwhelmed by too many experiences, disrupting their decision-making. The forgetting mechanism and evaluation metric for reflection memory are introduced by managing the stored information to address this issue. The agent calculates the score of each reflection memory. The bottom 10% of reflections will be eliminated. 








§ EXPERIMENTS

To verify the performance of our agent, we deployed our method in VLN-CE environments. This section outlines our experimental setup and implementation details and compares our performance against standard VLN-CE methods. We also highlight several notable features of LLM agents that could inform future research directions. Finally, we assess the impacts of our core methods and provide visual analyses.



 §.§ Experimental Setup

We conducted experiments on the VLN-CE dataset <cit.>, which includes 90 Matterport3D <cit.> scenes. Due to the extended response time of LLaMA, we randomly selected 200 tasks in unseen validation environments for our experiments. Following the methodologies of <cit.>, we used five evaluation metrics <cit.>: Navigation Error (NE), Trajectory Length (TL), Success Rate (SR), Oracle Success Rate (OSR), and Success Rate weighted by Path Length (SPL), with SR being the primary metric.



 §.§ Implementation Details

We utilize Vicuna-7b <cit.> as the scene describer to align visual modality information with natural language information. For path planning, considering the balance between performance and response time, we adopted GPT-3.5. As used in <cit.>, the Waypoint Predictor is employed with a candidate waypoint number set to 7. Our experiment is implemented in PyTorch, utilizing the Habitat simulator <cit.>, LangChain, and trained on two NVIDIA RTX 4090 GPUs.



 §.§ Comparison with Previous VLN-CE Methods

In line with previous research, we compare our agent with five previously published VLN-CE methods: Waypoint <cit.>, CMA <cit.>, BridgingGap <cit.>, LAW <cit.>, and Sim2Sim <cit.>. All experiments were conducted using the same setup. The results are presented in Table <ref>. Our Cog-GA demonstrated a notable advantage in Success Rate (SR) and Oracle Success Rate (OSR), indicating that the LLM-based agent performs better and effectively transfers its prior knowledge. However, it is essential to note that the trajectory length is significantly higher than other methods. That is attributed to the agent's conservative stopping mechanism, which prefers to get as close to the target point as possible.














 §.§ Ablation Experiments

To verify the effectiveness of each component of our method, we conducted ablation experiments based on the validation setup in unseen environments. These experiments focused on the influence of each element on Trajectory Length (TL), Success Rate (SR), and Oracle Success Rate (OSR). Specifically, we examined the reflection mechanism, the instruction rationalization mechanism, and the cognitive map. The results of the ablation experiments are presented in Table <ref>.



The results demonstrate that the instruction rationalization mechanism and the cognitive map significantly influence the agent's performance, while the reflection mechanism has a relatively lower impact. However, all components contribute to the overall effectiveness of the agent. The reflection mechanism, in particular, is primarily used for experience accumulation, suggesting that its importance will grow over the long term as more reflective memory is accumulated.



§ CONCLUSION

In this paper, we introduce a generative agent for VLN-CE that demonstrates the powerful ability of natural language to represent. By mimicking human navigation processes, the agent excels in performance. The simulation of brain navigation could bring an advantage for VLN-CE tasks. The cognitive map-based external memory enables the LLM agent to memorize spatial information. However, communication speed with LLMs is a significant hurdle when using these agents in robotic systems. Future efforts will aim to create a more efficient, high-performing generative agent and improve multimodal large models for vision-language navigation.




§ ACKNOWLEDGEMENTS

This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under  (Grants XDA0450200, XDA0450202), Beijing Natural Science Foundation (Grant L211023), and National Natural Science Foundation of China (Grants 91948303, 61627808)







IEEEtranS 























§ ALGORITHM PSEUDOCODE







§ TASK SETUP

1. Task Setup 

In Vision-Language Navigation in Continuous Environments (VLN-CE) <cit.>, agents must navigate through unseen 3D environments to specific target positions based on language instructions. These environments are considered as continuous open spaces. The agent selects a low-level action from an action sequence library at each step, given the instruction ℐ and a 360^∘ panoramic RGB-D observation 𝒴. Navigation is successful only if the agent selects a stop within 3 meters of the target location.

Recent VLN-CE solutions <cit.> have adopted a high-level waypoint search space approach. During navigation, the agent utilizes a Waypoint Predictor to generate a heatmap covering 120 angles and 12 distances, highlighting navigable waypoints. Each angle increment is 3 degrees, and the distances range from 0.25 meters to 3.00 meters, with 0.25-meter intervals corresponding to the turning angle and forward step size in the low-level action space. This approach translates the problem of inferring low-level controls into selecting an appropriate waypoint.





§ OPTIMAL PROMPT MECHANISM FOR LLMS IN NAVIGATION TASKS

2. Optimal Prompt Mechanism for LLMs in Navigation Tasks

During the development of the agent, we observed several intriguing features. The structural context is crucial for navigation tasks. To direct the LLM's focus toward navigation-related information, we categorized the information into three distinct types: objects, room types, and directions. Consequently, the context should be structured in the format 'Go (direction), Is (room type), See (objects).' Maintaining concise context is essential, as complex and miscellaneous contexts can disrupt the LLM's performance.

For waypoint selection, the clarity of surrounding environment descriptions also plays a significant role. We format the environment descriptions as 'In (direction), See (objects), Is (room type).' Clear and concise information reduces unnecessary processing burdens for LLMs and minimizes the risk of irrational outputs due to redundant input. However, a fully structured prompt alone is insufficient for VLN-CE tasks. Original instructions often involve multiple steps, and structural division of sub-instructions can lead to information loss and misdirection. Therefore, we introduced a guidance mechanism to provide structural information for the current target while supplementing sub-instructions. As detailed in section <ref>, we divided sub-targets into 'where' and 'what.' The 'where' targets involve switching environments based on room type, and the 'what' targets involve finding specific objects in the current environment. Thus, we constructed the structural guidance as 'You should try to go (where)' and 'You should try to find (what).' This guidance updates simultaneously with the rationalized sub-instruction to ensure coherence.



§ THE INFLUENCE OF INSTRUCTION QUALITY AND CONSTRUCTING BETTER SUB-INSTRUCTIONS

3. The Influence of Instruction Quality and Constructing Better Sub-Instructions

Our experiments highlighted the critical importance of instruction quality on navigation outcomes. This section analyzes how to enhance instruction quality during navigation and explores its implications for future work. As described in section <ref>, splitting instructions into multiple steps and continuously rationalizing each step has proven effective. For example, the rationalized sub-instruction 'Find the living room door and look for a sign to the kitchen.' yielded better results than the unprocessed sub-instruction 'Exit the living room.'. This finding reveals an interesting phenomenon: for an agent performing a task, the sequence of separated steps should maintain instructional coherence and constantly adapt the description of the target to the practical environment. Similar phenomena may also occur in human cognitive processes. Furthermore, performance improvements observed before and after splitting the original instruction demonstrate that LLMs have limited capacity to process long-term descriptive instructions. Long-term instructions can easily confuse the LLM by presenting multiple potential targets.



§ THE EVALUATION METRIC OF REFLECTION MEMORY

4. The evaluation metric of reflection memory

We define three parameters for each reflection memory: optimal distance, proximity, and repeatability. Optimal distance is the dynamic time warping (DTW) between the current and ground-truth navigation sequences. Repeatability counts how often similar memories occur, and proximity is the time between the memory and the current step. If a new memory is identical to an existing one, it won't be stored, and the current memory's proximity and repeatability will be updated. The score of each reflection memory is defined as follows:

    Score_m = |d_m - δ|/δ + t_m/T + r_m/max_r_n ∈ Rr_n

where d_m is the optimal distance, δ is the threshold parameter of the optimal distance, t_m is proximity, T is the current time step, r_m is repeatability, and R is the set of repeatability of reflection memories. The forgetting process will eliminate reflection memories with scores in the bottom 10%.





§ VISION-LANGUAGE NAVIGATION TASK SAMPLE

5. Vision-Language Navigation Task Sample

For the following figures, the left part is the first view image chosen by the agent, and the right part is the map for the task environment. The 'Action,' 'In,' and 'See' are the linguistic observations and movements for the first view image chosen by the agent.

TASK INSTRUCTION: Exit the living room and turn right into the kitchen. Turn left at the end of the counter and wait in the room across the hallway slightly to the left.



Step: 1 Action: go Left Front for 0.75 meters 

In: living room 

See: ['sofa', ' picture frame', ' lamp', ' bookshelf', ' window', ' clock', ' rug', ' painting', ' curtains', ' table']



Step: 2 Action: go Left Front for 1.25 meters 

In: living room 

See: ['grey wall', ' white couch', ' table', ' flat screen tv', ' white desk', ' window', ' fireplace', ' carpet', ' black tiles', ' lamp', ' mirror', ' plants']



Step: 3 Action: go Left Front for 2.5 meters 

In: living room 

See: ['couch', ' chairs', ' window', ' mirror', ' floor', ' walls', ' table', ' chairs', ' window', ' table']



Step: 4 Action: go Front for 1.75 meters	 

In: living room 	 

See: ['mirror', ' furniture', ' living room', ' spacious', ' white tiles', ' large mirror']



Step: 5 Action: go Right Rear for 1.75 meters 

In: living room

See: ['sofa', ' chandelier', ' fireplace', ' bookshelf', ' painting', ' rug', ' coffee table', ' curtains', ' lamp', ' mirror']



Step: 6 Action: go Right Front for 1.5 meters

In: kitchen

See: ['marble floor', ' blue tiles', ' grey walls', ' white refrigerator', ' silver handle', ' black stove', ' silver microwave', ' silver cabinet doors', ' white handles', ' white dishwasher', ' large mirror', ' silver frame', ' window']



Step: 7 Action: go Left Rear for 2.0 meters

In: living room

See: ['couch', ' coffee table', ' windows', ' wall', ' pictures', ' sink', ' counter', ' cabinets', ' floor', ' rug']



Step: 8 Action: go Front for 1.25 meters

In: kitchen

See: ['clock', ' hallway', ' kitchen', ' tiles', ' floor', ' curtains', ' walls', ' countertop', ' appliances', ' stove']



Step: 9 Action: go Front for 2.0 meters

In: kitchen 

See: ['kitchen', ' island counter', ' appliances', ' white walls', ' floor tiles', ' living area']



Step: 10 Action: go Right Front for 1.0 meters

In: living room

See: ['sofa', ' door', ' window', ' table', ' chair', ' lamp', ' bookshelf', ' painting', ' rug', ' clock']



Step: 11 Action: go Right Rear for 2.0 meters

In: kitchen

See: ['kitchen', ' center island, ' counter top', ' stools', ' refrigerator', ' oven', ' dishwasher', ' sink', ' table', ' lamp']



Step: 12 Action: go Behind for 1.75 meters

In: living room

See: ['cabinets', ' countertops', ' windows', ' sofa', ' coffee table', ' dining table']



Step: 13 Action: go Front for 1.5 meters

In: kitchen

See: ['windows', ' door', ' counter', ' mirror', ' sink', ' refrigerator', ' chairs', ' coffee table', ' bright', ' spacious']



Step: 14 Action: go Right Side for 1.75 meters

In: kitchen

See: ['kitchen island', ' marble countertops', ' induction cooktop', ' chairs', ' paintings', ' walls', ' flooring', ' tile']



Step: 15 Action: go Behind for 2.25 meters

In: living room

See: ['sofa', ' door', ' window', ' table', ' chair', ' lamp', ' painting', ' bookshelf', ' rug', ' clock']



Step: 16 Action: go Right Front for 2.0 meters

In: living room

See: ['sofa', ' door', ' window', ' table', ' chair', ' lamp', ' bookshelf', ' painting', ' rug', ' clock']



Step: 17 Action: go Right Rear for 2.0 meters

In: kitchen

See: ['kitchen counters', ' sink', ' stools', ' refrigerator', ' oven', ' stove', ' curtains', ' tiles', ' dining table', ' chairs']



Step: 18 Action: go Behind for 2.25 meters

In: kitchen 

See: ['kitchen', ' counter top', ' subway tiles', ' island', ' stools', ' fridge', ' oven', ' microwave', ' dishwasher', ' cabinets']



Step: 19 Action: go Front for 1.25 meters

In: living room

See: ['kitchen', ' dining area', ' living area', ' sofa', ' door', ' window', ' table', ' chair', ' stove', ' refrigerator']



Step: 20 Action: go Right Rear for 2.25 meters

In: living room

See: ['sofa', ' door', ' window', ' table', ' chair', ' lamp', ' bookshelf', ' painting', ' rug', ' clock']






































