\section{Introduction}

Object navigation \cite{COS-POMDP, ForeSI, VTNet, RES-StS} is a challenging task that requires an agent to find a target object in an unknown environment with first-person visual observations. 
Numerous techniques have been developed to advance this field by incorporating different inductive biases (Figure~\ref{compare_method_model} (a)) due to the task's complexity.
However, regrettably, the object navigation field does not form a unified inductive bias paradigm similar to the CV \cite{Tobias, convit} or NLP \cite{NLM_IB, sq2sq_IB} fields. Inspired by the flaw, through the induction and sublimation of the current mainstream methods, we propose a meta-ability decoupling (MAD) paradigm, hoping to unify and connect various object navigation methods. 

This paper involves two important new concepts: meta-ability and thinking. \textbf{Meta-ability} refers to every essential ability needed to complete a complex task. 
For instance, solving a mathematical problem requires the integration of various meta-abilities such as text comprehension, logical reasoning, and conceptual abstraction. Without these meta-abilities, relying solely on intuition is insufficient to complete complex tasks.
\textbf{Thinking} refers to the information abstraction for a certain ability. Typically, this abstraction is modeled end-to-end using neural networks. 

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{image/compare_method_model.pdf}}
\caption{(a) Existing methods directly improve the overall object navigation ability by introducing various inductive biases into the black box model. (b) Our proposed meta-ability decoupling (MAD) paradigm decomposes the overall object navigation ability into multiple meta-abilities, and designs specific inputs, thinking encoders, and rewards for each meta-ability.}
\label{compare_method_model}
\end{center}
\vskip -0.2in
\end{figure}

According to the definition of meta-ability and thinking, we summarize the current mainstream object navigation methods and identify their limitations. As shown in Figure~\ref{related_works}, object navigation methods are divided into four categories: association methods \cite{DOA, HOZ}, memory methods \cite{DUET, OMT}, deadlock-specialized methods \cite{TPN, MAAD} and SLAM methods \cite{SemExp, SSCNav}. The different inductive biases introduced by these four types of methods determine which meta-abilities are emphasized and which are overlooked. Therefore, the existing methods all attempt to use biased thinking to abstract the ultimate ability for object navigation (Figure~\ref{compare_method_model} (a)). Nevertheless, due to the sparsity and ambiguity of the reward signal, it is challenging for biased thinking to implicitly decouple complete meta-abilities which are crucial in object navigation. 


To address the above issues, we propose a meta-ability decoupling (MAD) paradigm (Figure~\ref{compare_method_model} (b)), which solves embodied AI tasks in five stages: (\romannumeral1) selecting meta-abilities based on prior knowledge; (\romannumeral2) determining the input features of each thinking according to the characteristics of its corresponding meta-ability; (\romannumeral3) designing suitable encoding networks for each thinking; (\romannumeral4) designing the collaboration modules between different thinking according to the characteristics of the task;
(\romannumeral5) designing rewards and punishments for each meta-ability. During this process, meta-abilities are decoupled in three aspects: input, encoding, and reward signals.  
In this paper, we primarily focus on the investigation of object navigation tasks, however, we believe that the MAD paradigm can be extended to other similar embodied AI tasks.

Guided by the MAD paradigm, we design a multiple thinking (MT) model for the object navigation task. First, we select five meta-abilities (explained in Sec.~\ref{sec:MAD}): intuition, search, navigation, exploration and obstacle. Subsequently, for these five meta-abilities, we use overall image features, object detection features, target-oriented memory, historical state memory, and obstacle location memory as input for corresponding thinking. Each thinking uses a simple encoding network with necessary inductive bias. 
Furthermore, we devise a multiple thinking collaboration (MTC) module to facilitate cooperation between the different meta-abilities.
Finally, meta-ability reward is designed to guide each thinking's abstract understanding for the corresponding meta-ability.

Extensive experiments on the AI2-Thor \cite{AI2-THOR} and RoboTHOR \cite{RoboTHOR} datasets show that our MAD paradigm not only outperforms SOTA methods on the typical object navigation task, but also on the zero-shot object navigation task. Moreover, an interpretability analysis of MT model based on MAD demonstrates that our method contributes significantly to both the interpretability and flexibility of object navigation tasks. Our contributions can be summarized as follows:

\begin{itemize}
\item We propose a general meta-ability decoupling (MAD) paradigm to generalize and unify various current object navigation approaches.
\item Following the MAD paradigm, we design a multiple thinking (MT) model for the object navigation task, which outperforms existing models in both typical and zero-shot object navigation tasks.
\item Our meta-ability interpretability framework provides a novel analytical mode for future researchers.
\end{itemize}

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[width=0.8\columnwidth]{image/related_works.pdf}}
\caption{\textbf{Summary of various object navigation methods}. We categorize the mainstream methods for object navigation into four classes, which achieve the enhancement of certain meta-abilities by improving the neural network.}
\label{related_works}
\end{center}
\vskip -0.2in
\end{figure}

\section{Related Works}

\subsection{Object Navigation}
\textbf{Target-specific typical object navigation tasks} require an agent to navigate to a known target object in an unknown environment. Some recent methods diligently improve the network or introduce prior knowledge in order to solve various problems in the object navigation task. We categorize these methods into four classes (Figure~\ref{related_works}): (\romannumeral1) association methods \cite{BRM, CKR, SP} which utilize object association or area association to enable the agent to build a relational graph model of the scene; (\romannumeral2) memory methods \cite{GBE, VGM} which depend on long-term explicit memory to more comprehensively consider historical information to make decisions; (\romannumeral3) SLAM methods \cite{GOSE, PONI} which build an agent-centric semantic map in real time; (\romannumeral4) deadlock-specialized methods \cite{SAVN, TPN} which use special mechanisms to help the agent escape from the local deadlock state.  Due to the lack of the meta-ability decoupling perspective, each class of methods only emphasize partial meta-abilities, resulting in a lack of comprehensive ability to solve complex tasks.

\textbf{Target-agnostic zero-shot object navigation tasks} are gaining increasing attention with the development of multimodal contrastive learning \cite{CLIP}. This task requires that the training environment shields the target objects for testing. Al-Halah et al. \citeyearpar{ZSEL}
mapped various modalities into the image-goal embedding space, thus adapting the image-goal navigation agent. 
Zhao et al. \citeyearpar{COS_ZSON} represented the object-target relationship as cosine similarity to alleviate the overfitting.
These zero-shot object navigation methods essentially extend the typical object navigation architecture by mapping the discrete class inputs to a continuous semantic space. 

\subsection{Decoupling Idea in Object Navigation}
Decoupling is a common concept in the field of artificial intelligence \cite{decouple_interpretable}, and it is also frequently observed in object navigation tasks. SemExp \cite{SemExp} decouples the continuous decision-making process into two discretized steps that are "where to look for an object?" and "how to navigate to (x, y)?". AVSW \cite{AVSW} decouples the environmental exploration from navigation to the target. ANS \cite{ANS} decouples the modeling of the environment from the end-to-end network using a real-time built semantic map. The aforementioned decoupling methods all split the end-to-end object navigation model into several independently trained models, thus losing the advantages of flexibility and simplicity. Our MAD paradigm improves the interpretability and generalizability of the model while maintaining end-to-end learning. 

\section{Meta-Ability Decoupling (MAD)}
\label{sec:MAD}
Object navigation is a complex long-distance decision-making task in the real world. Efficient and accurate navigation to the target object requires the assistance of multiple meta-abilities. We decouple the following five meta-abilities from the object navigation task based on existing object navigation methods and human experience: 
\begin{enumerate}[1)]
\setlength{\itemsep}{-0.1cm}
\item \textbf{Intuition Ability:} The ability to directly derive action decisions from raw image features.

\item \textbf{Search Ability:} The ability to look for the target object through knowledge association. 

\item \textbf{Navigation Ability:} The ability to navigate to the target position based on the target orientation information in memory.

\item \textbf{Exploration Ability:} The ability to efficiently and comprehensively acquire scene information.

\item \textbf{Obstacle Ability:} The ability to avoid colliding with obstacles.

\end{enumerate}
All five meta-abilities are present in current methods (Figure~\ref{related_works}), however, a single method only emphasizes certain meta-abilities. By clearly identifying and decoupling them, researchers can more easily combine the strengths of multiple approaches.


\section{Multiple Thinking (MT) Network}

The multiple thinking (MT) network is designed under the guidance of the MAD paradigm. As shown in Figure~\ref{architecture}, we design suitable input (Sec.~\ref{sec:Thinking Inputs}), encoding network (Sec.~\ref{sec:Thinking Embedding}), and reward (Sec.~\ref{sec:Meta-Ability Reward}) for each meta-ability. Additionally, we design a multiple thinking collaboration (MTC) module (Sec.~\ref{sec:MTC}) that interacts information between different types of thinking. 

\begin{figure*}[t]
\begin{center}
\centerline{\includegraphics[width=\textwidth]{image/architecture.pdf}}
\caption{\textbf{Model overview}. MTC: multiple thinking collaboration. Our multiple thinking (MT) model is primarily composed of five thinking modules and a MTC module, preceding the LSTM network. The colored boxes on the right match the details of thinking embedding on the left. (1) Intuition thinking takes image ResNet18 encoding as input. (2) Search thinking takes the object features extracted by DETR as input. (3) Navigation thinking takes the target orientation information at target-visible nodes as input. (4) Exploration thinking takes historical agent states as input. (5) Obstacle thinking takes the locations of known unreachable nodes as input.}
\label{architecture}
\end{center}
\vskip -0.2in
\end{figure*}


\subsection{Task Definition}

The agent is initialized to a random state $ s=\{x,y,\theta,\beta \}$ and random target object $p$. At each timestamp $t$, according to the single view RGB image $o_t$ and target $p$, the agent learns a navigation strategy $\pi(a_t|o_t, p)$, where $a_t\in A=\{MoveAhead$; $RotateLeft$; $RotateRight$;  $LookDown$;  $LookUp$; $Done\}$ and $Done$ is the output if the agent believes that it has navigated to the target location. Ultimately, if the agent is within a threshold (i.e., 1.5 meters \cite{TPN}) of the target and correctly detects it when $Done$ is output, the navigation episode is considered successful. 

Zero-shot object navigation task divides the target objects into a training set $P_{train} = \{p_1, p_2, \cdots, p_n\}$ and a test set $P_{test} = \{p_{n+1}, p_{n+2}, \cdots, p_{n+m}\}$. The objects in the test set are only available during the testing process.

\subsection{Thinking Inputs}
\label{sec:Thinking Inputs}

Each thinking’s input is the most important inductive bias for the corresponding meta-ability. Input features should be as concise as possible while meeting the requirements of the meta-abilities. As shown in the thinking boxes in Figure~\ref{architecture}, we select five specialized inputs from the agent’s available information based on the characteristics of the meta-abilities.

\begin{enumerate}[1)]\setlength{\itemsep}{-0.1cm}
\item \textbf{Intuition thinking inputs} $IT_i \in \mathbb{R}^{7\times 7\times 512}$ are extracted from the first-person perspective image using a fixed-weight ResNet18 \cite{ResNet18}.

\item \textbf{Search thinking inputs} $ST_i \in \mathbb{R}^{N\times 262}$ are the object visual and position features extracted from the image using DETR \cite{DETR}. 

\item \textbf{Navigation thinking inputs} $NT_i \in \mathbb{R}^{D_{n}\times 9}$ follow the target-oriented memory graph (TOMG) proposed in \cite{DAT}. Navigation thinking only focuses target-related information; thus the TOMG is composed of the target bounding box and the agent’s coordinates on the visited target-visible nodes.

\item \textbf{Exploration thinking inputs} $ET_i \in \mathbb{R}^{D_{e}\times 4}$ are the agent's historical positions and camera angles.

\item \textbf{Obstacle thinking inputs} $OT_i \in \mathbb{R}^{D_{o}\times 2}$ are the positions of known unreachable nodes. When the agent attempts to reach a certain node and fails, it will record that node as unreachable.
\end{enumerate}

$N$ is the number of objects. $D_n$, $D_e$, $D_o$ respectively represent the number of visited target-visible nodes, visited nodes and known unreachable nodes.

\subsection{Thinking Embedding}
\label{sec:Thinking Embedding}
Thinking embedding abstracts thinking inputs into the semantic space for decision making.
Past works \cite{CKR, DUET} have introduced various prior knowledge into thinking encoding networks to guide models' attention. However, our MT method only uses a minimal amount of encoding techniques based on the characteristics of each meta-ability, highlighting the advantage of the MAD paradigm itself.

\paragraph{Intuition Thinking}
A simple learnable pointwise convolution directly encodes the input ResNet features:
\begin{equation}
    IT_o = \delta (Conv(IT_i))
\end{equation}
where $Conv$ refers to the pointwise convolution and $\delta$ represents the ReLU nonlinearity \cite{ReLU}.

\paragraph{Search Thinking}
Search thinking aims to enable the agent to quickly capture the target with the fewest steps when the target is not in view. In order to have the object association ability, we adopt the unbiased directed object attention (DOA) graph $G_t \in \mathbb{R}^{N\times N}$ proposed in \cite{DOA} to assign weights to each object. We extract the object's attention weight vector $G_t^p$ from $G_t$ based on the target $p$, and assign it to each encoded object feature:
\begin{equation}
    ST_o = \delta (ST_i W^{ST})\odot G_{t}^{p}
\end{equation}
$W^{ST}$ is a learnable parameter matrix and $\odot$ allows each object feature to be multiplied by its corresponding attention coefficient.


\paragraph{Navigation Thinking}
Navigation thinking requires the ability to memorize, locate and navigate to the target. We borrow the target-aware multi-scale aggregator (TAMSA) proposed in \cite{DAT} to map the target observation information of different positions into the target orientation relative to the current state.

First, the decisions (e.g. rotate right) are made relative to the current agent’s state $(x_c, y_c, \theta _c , \beta _c)$, so we self-center the agent's states $(x_i, y_i, \theta _i , \beta _i)$ stored in TOMG.
\begin{equation}
\begin{aligned}
(\widetilde {x}_i, \widetilde {y}_i)&=(x_i,y_i)-(x_c,y_c)\\
(\widetilde {\theta} _i^{x}, \widetilde {\beta } _i^{x})&=sin((\theta _i,\beta _i)-(\theta  _c,\beta _c))\\
(\widetilde {\theta} _i^{y}, \widetilde {\beta } _i^{y})&=cos((\theta _i,\beta _i)-(\theta _c,\beta _c))\;\;\;i\in \Delta _M
\end{aligned}
\end{equation}
where $\Delta _M$ represents the index collection of target-visible nodes. To ensure that the angle and position coordinates have the same order of magnitude, we use $sin$ and $cos$ to normalize the angle coordinates to $[-1, 1]$. After this egocentric coordinate transformation, we obtain egocentric TOMG features $ \widetilde{NT_i} \in \mathbb{R}^{D_{n}\times 11}$. Similarly, the agent states in exploration thinking and obstacle thinking also need egocentric transformation as described above.

The subsequent encoding process is represented as:
\begin{gather}
NT_o = H^{T}F_{NT}(\widetilde{NT_i})\odot F_{E}(E) \\
H = \sum_{j=1}^{3}TCN_j(\widetilde{NT_i})
\end{gather}
$H \in \mathbb{R}^{D_{n}\times 1}$ is obtained by summing different scale kernels that are generated by using the multi-scale temporal convolution networks (TCNs) to process $\widetilde{NT_i}$.
$F_{NT}(\cdot)$ maps $\widetilde{NT_i}$ to a higher-dimensional feature space. Since navigation thinking needs to adaptively change when searching for different targets, the one-hot target index $E$ is encoded by two fully connected (FC) layers $F_{E}(\cdot )$ to generate a channel-wise activation vector which recalibrates channel-wise feature responses.

\paragraph{Exploration Thinking}

We hope that the agent can use exploration thinking to more efficiently explore the environment and avoid repeated exploration. After self-centering the agent's state, we also introduce two inductive biases by polarizing the coordinates. (\romannumeral1) Through the distance of polar coordinates, the network could learn that historical states closer to the current node are more important. (\romannumeral2) Through the angle of polar coordinates, the network could learn that traveling in the direction of less exploration can gain more knowledge of the scene. Subsequently, we use two FC layers $F_{ET}(\cdot)$ and a global average pooling layer to obtain the output of exploration thinking.
\begin{equation}
ET_o=\frac{1}{D_e} \sum_{l=1}^{D_e}F_{ET}(g(ET_i\left \langle l \right \rangle))
\end{equation}
where $\left \langle l \right \rangle$ retrieves the feature of the $l$-th node from the historical memory graph, and $g(\cdot)$ represents the process of converting a cartesian coordinate system to a polar coordinate system.

\paragraph{Obstacle Thinking}
Previous approaches commonly suffered from the issue of repeatedly colliding with the same obstacle, leading to deadlock. Our obstacle thinking helps the agent quickly escape from deadlock states by memorizing collided obstacles. The overall encoding process is similar to that of exploration thinking. 
\begin{equation}
OT_o=\frac{1}{D_o} \sum_{l=1}^{D_o}F_{OT}(g(OT_i\left \langle l \right \rangle))
\end{equation}

Dropout layer is added after the more complex intuition thinking, search thinking, and navigation thinking in the above five thinking encoding networks.

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[width=0.85\columnwidth]{image/MTC.pdf}}
\caption{\textbf{Multiple thinking collaboration (MTC) module between two thinking.}  The increase in the number of thinking does not affect the overall structure. We first extract the holistic-thinking feature from the outputs of multiple thinking. Then, the channel activation vector is generated for each thinking, which recalibrates the thinking features.}
\label{MTC}
\end{center}
\vskip -0.2in
\end{figure}

\subsection{Multiple Thinking Collaboration (MTC)}
\label{sec:MTC}
Although we decouple the meta-abilities required for object navigation, cooperation between the meta-ability thinking is still necessary. For instance, when the search thinking discovers that the target is to the right of the agent, the obstacle thinking needs to give the obstacles on the right more attention.
Therefore, we design a multiple thinking collaboration (MTC) module (Figure~\ref{MTC}) to transmit shared information between different thinking.

The MTC module primarily recalibrates the channel weights of each thinking using the condensed information from all thinking. Initially, we squeeze the outputs of multiple thinking into a holistic-thinking feature representation $Z$:
\begin{equation}
Z = \delta (W_Z\left [ IT_o, ST_o, NT_o, ET_o, OT_o \right ] +b_Z)
\end{equation}
Then, excitation signals for each thinking are generated to recalibrates each thinking output $\mathcal{X}T_o$:
\begin{equation}
\mathcal{X}T_c = \mathcal{X}T_o \odot \sigma (W_{\mathcal{X}T}Z+b_{\mathcal{X}T}) \;\;\; \mathcal{X}\rightarrow I,S,N,E,O 
\end{equation}
where $\sigma(\cdot)$ represents the sigmoid activation function.

\subsection{Policy Learning}
After holistic-thinking recalibration, all thinking features need to be integrated into a unified representation vector:
\begin{equation}
G=\delta (W_G[IT_c, ST_c, NT_c, ET_c, OT_c]_{LN}+b_G)
\end{equation}
where $[\cdot]_{LN}$ concatenates the final output features of each thinking and uses layer normalization to stabilize the forward input distribution and backpropagation gradient. Finally, the multiple thinking joint representation $G$ is used to learn an LSTM \cite{LSTM} action policy $\pi(a_t|G_t, p)$. Following the previous works \cite{SP, DOA}, we treat this task as a reinforcement learning problem and utilize the asynchronous advantage actor-critic (A3C) algorithm \cite{A3C}. 


\subsection{Meta-Ability Reward}
\label{sec:Meta-Ability Reward}
Our model is supervised by two types of reward: base reward $R_B$ and meta-ability reward $R_{MA}$. Similar to the previous work \cite{HOZ}, $R_B$ is composed of three parts. (\romannumeral1) We penalize each step with a small negative reward -0.01. (\romannumeral2) To encourage movement, if the agent outputs $MoveAhead$, a positive reward of 0.01 is given. (\romannumeral3) If any object instance from the target object category is reached within a certain number of steps, the agent receives a large positive reward 5.0. Meta-ability reward is designed for the goals that each meta-ability needs to achieve. 
\begin{equation}
R_{MA} = R_s + R_n + R_e + R_o
\end{equation}
\paragraph{Search Reward $R_s$}
If the target object is correctly identified in the field of view, $R_s=0.01$, otherwise $R_s=0$.
\paragraph{Navigation Reward $R_n$}
Once the target has been located, if the chosen action allows the agent to move closer to the target, $R_n=0.01$, otherwise $R_n=0$.
\paragraph{Exploration Reward $R_e$}
If the agent repeatedly reaches the same state, $R_e=-0.01$, otherwise $R_e=0$.
\paragraph{Obstacle Reward $R_o$}
If the agent collides with an obstacle, $R_o=-0.01$, otherwise $R_o=0$.

$R_{MA}$ enables each meta-ability thinking to more quickly capture the direction of learning. Accordingly, during the initial $C$ training episodes when guiding the model’s learning direction, the model is supervised by both meta-ability reward $R_{MA}$ and base reward $R_B$. Afterwards, when better task performance metrics are desired, the model receives supervision solely from the base reward $R_B$.  



\section{Experiment}
\subsection{Experimental Setup}
\paragraph{Datasets}
AI2-Thor \cite{AI2-THOR} and RoboTHOR \cite{RoboTHOR} are our primary experimental platforms. AI2-Thor includes 30 different floorplans for each of 4 room layouts: kitchen, living room, bedroom, and bathroom. For each scene type, we use 20 rooms for training, 5 rooms for validation, and 5 rooms for testing. RoboTHOR consists of a set of 89 apartments, 75 of which are accessible. we use 60 for training and 15 for validation. RoboTHOR is a more complex version of the AI2-Thor environment, with 2.4 times larger floor area and 5.5 times longer path length. 

For zero-shot object navigation, we re-split the widely used 22 target classes \cite{MJO, COS_ZSON} into 18/4 seen/unseen and 14/8 seen/unseen classes. We train the model with seen object classes as the targets and test the model with unseen object classes as the targets. 

\paragraph{Evaluation Metrics}
We use the success rate (SR), success weighted by path length (SPL) \cite{SPL} metrics to evaluate our method. SR indicates the success rate of the agent in completing the task, which is formulated as $SR = \frac{1}{F}\sum_{i=1}^{F}Suc_i$, where $F$ is the number of episodes and $Suc_i$ indicates whether the $i$-th episode succeeds. SPL considers the path length more comprehensively and is defined as $SPL = \frac{1}{F}\sum_{i=1}^{F}Suc_{i}\frac{L_i^*}{max(L_i, L_i^*)}$, where $L_i$ is the path length taken by the agent and $L_i^*$ is the theoretical shortest path. 

\paragraph{Implementation Details}
The model with only intuition thinking 
(IT) and base reward $R_B$ is our baseline. We train our model with 18 workers on 2 RTX 2080Ti Nvidia GPUs, in a total of 3M navigation episodes. The dropout rate is set to 0.3, and the meta-ability reward $R_{MA}$ is only utilized in the first 0.2M ($C$) episodes. We report the results for all targets (ALL) and for a subset of targets ($L\ge 5$) with optimal trajectory lengths greater than 5.


\begin{table}[t]
\centering
\small
\setlength\tabcolsep{5pt}
\caption{Ablation experiments for each meta-ability. Removing a meta-ability means removing the corresponding thinking and reward for the meta-ability.}
\label{tab:ablation}
\begin{tabular}{ccccc|cc|cc}
\hline
\multirow{2}{*}{IT} & \multirow{2}{*}{ST} & \multirow{2}{*}{NT} & \multirow{2}{*}{ET} & \multirow{2}{*}{OT} & \multicolumn{2}{c|}{ALL (\%)} & \multicolumn{2}{c}{$L\ge 5$ (\%)} \\
 &  &  &  &  & SR$\uparrow$ & SPL$\uparrow$ & SR$\uparrow$ & SPL$\uparrow$ \\ \hline \hline
\checkmark &  &  &  &  & 43.48 & 18.91 & 30.36 & 14.34 \\
\checkmark & \checkmark &  &  &  & 75.94 & 42.77 & 68.24 & 43.28 \\
\checkmark & \checkmark & \checkmark &  &  & 79.62 & 46.13 & 72.96 & 45.93 \\
\checkmark & \checkmark & \checkmark & \checkmark &  & 81.97 & 48.75 & 75.54 & 48.95 \\ \hline
\checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \textbf{83.14} & \textbf{50.23} & \textbf{77.03} & \textbf{50.88} \\ \hline
\end{tabular}
\end{table}

\begin{table}[t]
\small
\centering
\setlength\tabcolsep{3.7pt}
\caption{Ablation experiments for the multiple thinking collaboration (MTC) module and meta-ability reward.}
\label{tab:MTC_MAR}
\begin{tabular}{c|l|cc|cc}
\hline
\multirow{2}{*}{ID} & \multirow{2}{*}{Method} & \multicolumn{2}{c|}{ALL (\%)} & \multicolumn{2}{c}{$L\ge 5$ (\%)} \\
 &  & SR$\uparrow$ & SPL$\uparrow$ & SR$\uparrow$ & SPL$\uparrow$ \\ \hline \hline
1 & Complete MT & 83.14 & 50.23 & 77.03 & 50.88 \\ \hline
2 & MT $\rightarrow$ No MTC & 82.26 & 50.14 & 76.17 & 50.52 \\
3 & MT $\rightarrow$ No $R_{MA}$ & 81.96 & 49.54 & 76.22 & 49.93 \\
4 & $R_B+R_{MA}$ (All Episodes) & 81.31 & 48.29 & 75.36 & 48.44 \\ \hline
\end{tabular}
\end{table}

\subsection{Ablation Experiments}
\paragraph{Meta-Ability Ablation}
The object navigation task is decomposed into a total of five meta-abilities, which are ablated in Table~\ref{tab:ablation}. Based on the MAD structure, search ability is the most important meta-ability, followed by navigation ability, with exploration ability and obstacle ability playing a supportive role. 

\paragraph{MTC and Meta-Ability Reward Ablation}
Table~\ref{tab:MTC_MAR} shows the ablation results where the MTC module and the meta-ability reward $R_{MA}$ are removed in the second and third rows, respectively. We observe that the MTC module has a greater impact on SR, and the meta-ability reward improves both SR and SPL. The results of the fourth row in Table~\ref{tab:MTC_MAR}, in which both base reward $R_B$ and meta-ability reward $R_{MA}$ are used throughout the entire training process, suggest that using meta-ability reward in the later stages of training can divert the model's pursuit of the final goal (finding the object via the shortest path) and result in a disconnection between the reward and actual performance.

\subsection{Comparative Analysis of Different Targets}
Figure~\ref{each_object_sr} compares the SR of our MT method and the current SOTA method (DOA \cite{DOA}) for each target object. Previous methods perform poorly for small objects and objects in complex environments (e.g. bedroom). The five target objects (labeled in red) that benefit most from our method are mostly previously unresolved targets. The five target objects (labeled in blue) that benefit least from our method are mostly common in the simpler kitchen scene. It is observable from the pie chart that our MT model makes a much greater overall contribution to SR improvement in complex scenes (e.g. bedroom, living room) compared to simple scenes (e.g. kitchen, bathroom). These findings indicate that our MT method can address multifaceted decision-making challenges in complex environments via flexible meta-abilities. More experiments are in Appendix~\ref{sec:D} and~\ref{sec:Appendix:E}.

\begin{figure}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{image/each_object_sr.pdf}}
\caption{\textbf{Comparison of our MT method with the DOA method in terms of SR index for each individual target.} 
The red and blue markers indicate the targets with highest and lowest performance improvement of the MT method respectively. The pie chart shows the contribution of each scene to overall SR improvement. Subsequently, two objects with highest contribution from each scene are plotted in a bar chart.}
\label{each_object_sr}
\end{center}
\vskip -0.2in
\end{figure}

\begin{table}[t]
\centering
\scriptsize
\setlength\tabcolsep{3pt}
\caption{Comparison with target-specific SOTA methods on the AI2-Thor / RoboTHOR  datasets.}
\label{tab:SOAT_standard}
\begin{tabular}{c|c|cc|cc|c}
\hline
\multirow{2}{*}{ID} & \multirow{2}{*}{Method} & \multicolumn{2}{c|}{ALL (\%)} & \multicolumn{2}{c|}{$L\ge 5$ (\%)} & \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Episode \\ Time (s)$\downarrow$\end{tabular}}  \\
 &  & SR$\uparrow$ & SPL$\uparrow$ & SR$\uparrow$ & SPL$\uparrow$ &   \\ 
 \hline \hline
\multirow{2}{*}{\uppercase\expandafter{\romannumeral1}} & SSCNav & 77.14/38.12 & 31.09/14.10 & 71.73/33.46 & 34.33/11.04 & 1.34/4.14  \\
 & PONI & 78.58/38.42 & 33.78/16.30 & 72.92/34.72 & 36.40/13.22 & 1.59/4.58  \\ \hline
\multirow{2}{*}{\uppercase\expandafter{\romannumeral2}} & OMT & 71.13/32.17 & 37.27/20.09 & 61.94/25.33 & 38.19/18.16 & 0.64/2.01  \\
 & VGM & 73.95/35.82 & 40.69/23.71 & 64.07/27.22 & 40.73/19.54 & 0.73/2.46  \\ \hline
\uppercase\expandafter{\romannumeral3} & TPN & 67.32/30.51 & 37.01/18.62 & 58.13/23.89 & 35.90/14.91 & 0.24/0.77  \\ 
\hline
\multirow{3}{*}{\uppercase\expandafter{\romannumeral4}} & HOZ & 68.53/31.67 & 37.50/19.02 & 60.27/24.32 & 36.61/14.81 & 0.28/0.81  \\
 & VTNet & 72.24/33.92 & 44.57/23.88 & 63.19/26.77 & 43.84/19.80 & 0.32/1.33  \\
 & DOA & 74.32/36.22 & 40.27/22.12 & 67.88/30.16 & 40.36/18.32 & 0.33/1.25  \\ \hline
\rowcolor{gray!30}
\uppercase\expandafter{\romannumeral5} & \textbf{MT} & \textbf{83.14/42.80} & \textbf{50.23/29.07} & \textbf{77.03/37.85} & \textbf{50.88/23.16} & 0.35/1.20  \\ 
\hline
\end{tabular}
\end{table}



\begin{table}[t]
\small
\caption{Comparison with target-agnostic 
zero-shot SOTA methods on the AI2-Thor datasets. }
\label{tab:SOTA_ZSON}
\begin{tabular}{c|c|cccc}
\hline
\multirow{3}{*}{Method} & \multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}Seen/Unseen\\ split\end{tabular}} & \multicolumn{4}{c}{Unseen Classes} \\ \cline{3-6} 
 &  & \multicolumn{2}{c|}{ALL (\%)} & \multicolumn{2}{c}{$L\ge 5$ (\%)} \\
 &  & SR$\uparrow$ & \multicolumn{1}{c|}{SPL$\uparrow$} & SR$\uparrow$ & SPL$\uparrow$ \\ \hline \hline
Random & 18/4 & 9.76 & \multicolumn{1}{c|}{2.03} & 0.82 & 0.27 \\
ZER & 18/4 & 31.28 & \multicolumn{1}{c|}{15.06} & 25.74 & 14.60 \\
ZSON & 18/4 & 57.32 & \multicolumn{1}{c|}{20.94} & 46.43 & 21.78 \\ \hline
\rowcolor{gray!30}
\textbf{MT-ZS} & 18/4 & \textbf{68.61} & \multicolumn{1}{c|}{\textbf{27.95}} & \textbf{57.53} & \textbf{29.28} \\ \hline
Random & 14/8 & 7.70 & \multicolumn{1}{c|}{3.19} & 0.44 & 0.08 \\
ZER & 14/8 & 24.62 & \multicolumn{1}{c|}{10.42} & 14.33 & 8.99  \\
ZSON & 14/8 &  52.74 &  \multicolumn{1}{c|}{18.11} &  33.53 & 14.38  \\ \hline
\rowcolor{gray!30}
\textbf{MT-ZS} & 14/8 & \textbf{62.40} & \multicolumn{1}{c|}{\textbf{24.08}} &  \textbf{46.58} & \textbf{23.76} \\ \hline
\end{tabular}
\end{table}


\subsection{Comparisons with the State-of-the-Art}

\paragraph{Target-Specific Typical Object Navigation}
In Table~\ref{tab:SOAT_standard}, our MT model is compared with the four categories of SOTA models. \textbf{(\uppercase\expandafter{\romannumeral1}) SLAM methods.} The real-time construction of a semantic map and sub-goal path planning technique enhance the interpretability of these methods. However, due to the significant cost of exploring the environment, the time required for each episode is several times that of other methods. \textbf{(\uppercase\expandafter{\romannumeral2}) Memory methods.} The explicit memory of long-term historical information enhances the model's exploration and navigation ability. Despite this, existing memory methods have a large amount of redundancy, resulting in poor generalization. 
\textbf{(\uppercase\expandafter{\romannumeral3}) Deadlock-specialized methods.} Deadlock states occur frequently while navigating. Although incorporating a deadlock-specialized module resolves some issues, it disrupts the overall cohesiveness of model training. 
\textbf{(\uppercase\expandafter{\romannumeral4}) Association methods.} Object or region association is the most common inductive bias. Narrowly, association methods only serve to enhance search ability, with minimal effect on other abilities. 
Under the guidance of the MAD paradigm, our MT method explicitly decouples the various meta-abilities of the object navigation task, theoretically unifying the above four types of models. In comparison to the SOTA method (DOA \cite{DOA}) with similar computational complexity, our MT method brings an overall 8.82/6.58 and 9.96/6.95 improvement in SR and SPL (AI2-Thor / RoboTHOR, \%), respectively.

\paragraph{Target-Agnostic Zero-Shot Object Navigation}
In contrast to typical object navigation tasks, the target objects in the zero-shot task are not visible during training. Consequently, we replace the object one-hot encoding in the MT model with the cosine similarity of glove embedding to the target \cite{COS_ZSON}, resulting in the MT-ZS model. In comparison with the CLIP-based ZER model \cite{ZER} and the class-unrelated ZSON model \cite{COS_ZSON}, our MT-ZS model based on the MAD paradigm exhibits clear advantages (Table~\ref{tab:SOTA_ZSON}). The success of our MAD paradigm in the zero-shot task demonstrates its effectiveness for embodied AI tasks with high generalization difficulty as well. More experiments are in Appendix~\ref{sec:C}.

\begin{figure*}[t]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=\textwidth]{image/visual_ai2thor.pdf}}
\caption{\textbf{We visualize the average activation of each thinking’s neurons during navigation.} The depth of the arrow color represents the average value of the current thinking’s output neurons, corresponding to the line chart above. The blue pentagram signifies the step in the path where thinking is most active.}
\label{visual_ai2thor}
\end{center}
\vskip -0.2in
\end{figure*}

\subsection{Meta-ability Qualitative Analysis}
\label{sec:Meta-ability Qualitative Analysis}
Our MT model makes decisions based on the synthesis of various meta-abilities during navigation. In Figure~\ref{visual_ai2thor}, we visualize the mean value of the thinking output neurons $\mathcal{X}T_c$ at each step to explore how each thinking influences model inference in different scenarios.  Intuition thinking exhibits no discernible pattern, so in this case we only illustrate the other four thinking. 
(a) Search thinking is activated when the target or target-related objects are observed, and becomes increasingly active as the target is approached.
(b) When the target object is suddenly lost from the agent's field of view, the level of navigation thinking becomes greatly heightened, enabling the agent to quickly reacquire the target object.
(c) Continuous forward motion quickly activates exploration thinking. 
(d) Obstacle thinking is maximally activated when the agent encounters an obstacle, and the level of activation gradually decreases as the distance from the obstacle increases.
Each thinking’s excitation provides a clearer explanation for the model's decision-making process based on meta-abilities. More analysis is in Appendix~\ref{Appendix:F}.

\begin{table}[t]
\scriptsize
\centering
\setlength\tabcolsep{4.5pt}
\caption{Quantitative comparison of meta-abilities across different models.}
\label{tab:meta-ability-metics}
\begin{tabular}{c|cccc}
\hline
\multirow{2}{*}{Method} & \multicolumn{4}{c}{ALL (\%)} \\
 & SSR (S)$\uparrow$ & NSNLP (N)$\uparrow$ & REP (E)$\downarrow$ & CP (O)$\downarrow$ \\ \hline
Baseline & $91.35$ & $23.15$ & $5.28$ & $12.74$ \\
DOA & $95.82_{(+4.47)}$ & $44.11_{(+20.96)}$ & $7.14_{(+1.86)}$ & $10.26_{(-2.48)}$ \\
\rowcolor{gray!30}
MT & $97.76_{(+6.14)}$ & $51.39_{(+28.24)}$ & $4.03_{(-1.25)}$ & $4.93_{(-7.81)}$ \\ \hline
\end{tabular}
\end{table}
\subsection{Meta-Ability Quantitative Analysis}
\paragraph{Meta-Ability Metrics}
In order to quantitatively evaluate the meta-abilities of each model, we define four meta-ability metrics: (\romannumeral1) search success rate (SSR): the success rate in finding the target; (\romannumeral2) navigation success weighted by navigation path length (NSNPL): SPL during the navigation phase after finding the target; (\romannumeral3) repeated exploration probability (REP): probability of reaching the same state repeatedly; (\romannumeral4) collision probability (CP): proportion of actions resulting in collision with obstacles. Larger SSR and NSNPL values indicate stronger search and navigation abilities, while smaller REP and CP values indicate stronger exploration and obstacle abilities. More detailed explanations are in Appendix~\ref{sec:B}.

\paragraph{Analysis}
As shown in Table~\ref{tab:meta-ability-metics}, our MT model performs significantly better in each meta-ability than the other models. Current SOTA method DOA primarily utilizes object association to enhance search ability, however, REP indicates that the exploration ability of the DOA method has decreased relative to the baseline model. This phenomenon suggests that without decoupling meta-abilities, enhancing one meta-ability may lead to weakening other meta-abilities. It is noteworthy that our MT model only employs a small fraction of the inductive bias used in the DOA model to enhance search ability (Sec.~\ref{sec:Thinking Embedding}), yet the search ability of the MT model outperforms that of the DOA model. This finding leads us to believe that the thinking specificity promoted by the MAD paradigm can amplify the impact of each inductive bias on the corresponding meta-ability.


\section{Limitation}
There are still some limitations to this paper. (\romannumeral1) The selection of meta-abilities depends on human experience, so how to decouple the more abstract meta-abilities is still an open problem.  (\romannumeral2) MAD is only applied and experimented on the object navigation task in this paper, and we expect that researchers can expand it to more embodied AI tasks. (\romannumeral3) How meta-ability thinking affects the model’s decision-making still has many directions worthy of exploration. 

\section{Conclusion}

This paper proposes the meta-ability decoupling (MAD) paradigm, which guides researchers to design and analyze object navigation models from the perspective of meta-abilities. Based on MAD, we design the multiple thinking (MT) model, which significantly outperforms SOTA methods in both typical and zero-shot object navigation tasks. 
Additionally, we conduct a qualitative and quantitative interpretability analysis of the MT model at the meta-ability level.
Beyond the object navigation, the underlying principles are theoretically generalizable to other embodied AI tasks.


\nocite{langley00}

