Overcoming Motion Drifts in Visual Navigation: Action Adaptive Policy
Table of Contents
- Introduction
- Understanding the Problem of Robust Visual Navigation
- Impact of Motion Drifts on Navigation
- Examples of Motion Drifts in Novel Scenes
- Learning a Robust Policy for Navigation
- Point Navigation Task
- Object Navigation Task
- Encoding Action Impact in Navigation
- Implicitly Encoding Actions' Behavior
- Designing Action Embeddings for Impact Representation
- Overcoming Fixed Mapping Issue with Transformer Model
- Introduction to Transformer Model
- Action Adaptive Policy with Transformer Model
- Experimental Evaluation in Different Environments
- RoboTHOR Environment
- Habitat Environment
- Petting Zoo Environment
- Results and Comparison with Baseline Models
- Point Navigation Task Results
- Object Navigation Task Results
- Evaluation with Restricted Turning Actions
- Qualitative Results and Analysis
- Qualitative Results for Point Navigation Task
- Qualitative Results for Object Navigation Task
- Exploratory Experiment on Real-World Object Navigation
- Conclusion and Future Work
- References
Introduction
Visual navigation is an essential task for agents operating in novel environments. However, the presence of motion drifts can significantly impact an agent's ability to navigate effectively. In this work, we address the problem of robust visual navigation, where agents learn to encode the impact of their actions to overcome motion drifts. We propose a novel approach that allows agents to adapt to unexpected drifts and improve their navigation performance.
Understanding the Problem of Robust Visual Navigation
Impact of Motion Drifts on Navigation
Motion drifts refer to changes in how an agent's actions influence the environment. These drifts can arise due to various factors, such as different flooring materials or obstructions on the robot's wheels. For example, an agent trained on carpets may struggle to navigate smoothly on bamboo floors. Similarly, the agent's movement and rotation can be severely disturbed when encountering tape, Dust, or hair.
Examples of Motion Drifts in Novel Scenes
To study the problem of robust visual navigation, we consider two tasks: point navigation and object navigation. In point navigation, the agent aims to reach a specific location in the environment. In object navigation, the agent needs to locate and navigate to a specific object. During training and testing, we sample movement and rotation drifts from novel distributions that the agent has not seen before.
Learning a Robust Policy for Navigation
To train our agent's policy, we incorporate the observed impact of action on drifts. We sample movement and rotation drifts at the beginning of each episode, which are usually larger in magnitude than standard actuator noise. The agent needs to learn a robust policy that considers these drifts and adapts its actions accordingly.
Point Navigation Task
In the point navigation task, the agent must navigate to a specific location in the environment. We use consecutive visual observations, task goals, and the previous action to summarize the state change caused by an action. The Action Impact encoder produces action embeddings that encode the observed impact of actions. We use a recurrent neural network and a Transformer-Based order invariant head to make action decisions based on expected impacts.
Object Navigation Task
In the object navigation task, the agent's goal is to locate and navigate to a specific object in the environment. We adapt the same approach used in the point navigation task to encode action impacts and make informed action decisions. The Action Impact encoder and the order invariant head enable the agent to learn a robust policy for object navigation.
Encoding Action Impact in Navigation
To address motion drifts, we propose to implicitly encode the actual impact of actions on the environment. Instead of focusing on the semantics of actions, we want the agent to learn the expected outcome of its actions based on their observed impact. We design the agent's action embedding to represent the observed impact of actions in each dimension. By recording the outcome of each action at the corresponding dimension of the embedding, the agent can choose actions based on their expected impacts.
Overcoming Fixed Mapping Issue with Transformer Model
While the Action Impact embedding idea is promising, it is insufficient to produce robust agents with traditional linear actor models. The fixed mapping between actions and their impact limits the agent's ability to adapt to different drifts. To address this issue, we propose the use of a Transformer model as our actor.
Introduction to Transformer Model
The Transformer model utilizes self-Attention mechanisms and Parallel computation to capture dependencies between different parts of the input. This enables the agent to weigh the impact of different actions and make informed decisions based solely on the expected impacts.
Action Adaptive Policy with Transformer Model
In our proposed action adaptive policy (AAP), we incorporate the Transformer model into our agent's policy network. The Action Impact encoder produces action embeddings, while the order invariant head uses the Transformer model to make action decisions based on the expected impacts. By breaking the fixed mapping issue, our AAP requires the agent to choose actions solely based on their impacts, resulting in more robust navigation.
Experimental Evaluation in Different Environments
To evaluate the effectiveness of our proposed approach, we conducted experiments in three different environments: RoboTHOR, Habitat, and Petting Zoo. These environments provide a diverse set of scenarios to test the robustness of our agent's navigation policy.
RoboTHOR Environment
In the RoboTHOR environment, our AAP consistently outperforms other baseline models across all unseen rotation drifts. Even for extreme movement drifts, our model performs significantly better than the competing baselines.
Habitat Environment
In the Habitat environment, our AAP surpasses the performance of the embodied clip baseline in point navigation tasks across all unseen rotation drifts up to 90 degrees. This highlights the ability of our model to adapt to different drifts and navigate effectively.
Petting Zoo Environment
We also conducted experiments in a simple object push task in the Petting Zoo environment. Our AAP demonstrates robust performance compared to the baseline models, solving the tasks with high success rates after 100 million training steps.
Results and Comparison with Baseline Models
In our experiments, we compared the performance of our AAP with several baseline models. The results consistently Show that our proposed approach outperforms the baselines in both the point navigation and object navigation tasks.
Point Navigation Task Results
Our AAP exhibits superior performance across all unseen rotation drifts, even up to 180 degrees. It surpasses the standard baseline agent, model-free meta-reinforcement learning agent, and the framework that aims to learn a latent code for environment modeling.
Object Navigation Task Results
For the object navigation task, our AAP consistently outperforms all the competing baseline models. It demonstrates a higher success rate in locating and navigating to the target objects compared to the baselines.
Evaluation with Restricted Turning Actions
We also evaluated the performance of our AAP when certain turning actions were disabled. In the Scenario where only right turn actions were allowed, our model adapted its navigation strategy accordingly. Similarly, when only left turn actions were allowed, our AAP exhibited robust behavior and achieved the task objectives successfully.
Qualitative Results and Analysis
We provide qualitative results and analysis to showcase the effectiveness and understanding of our AAP model in both the point navigation and object navigation tasks.
Qualitative Results for Point Navigation Task
We present qualitative results for the point navigation task, demonstrating the agent's ability to adapt its actions based on the observed drifts. The agent intelligently avoids obstacles and corners, utilizing the appropriate actions to reach its target location smoothly.
Qualitative Results for Object Navigation Task
In the object navigation task, the agent shows an understanding of the environment and navigates strategically to locate the target objects. It bypasses walls and obstacles, and even with significant drifts, it successfully completes the task.
Exploratory Experiment on Real-World Object Navigation
To evaluate the robustness of our AAP in a real-world setting, we conducted an exploratory experiment on object navigation tasks. The results demonstrate that our model performs well compared to the embodied clip baseline, even with significant drifts in rotation and movement.
Conclusion and Future Work
In this work, we addressed the problem of robust visual navigation by incorporating the observed impact of actions to overcome motion drifts. Our proposed action adaptive policy, which combines the Transformer model with action impact encoding, demonstrated superior performance compared to baseline models in various environments. We provided qualitative analysis and highlighted the agent's understanding and adaptability in different navigation tasks. In the future, we plan to explore further improvements to our model and extend the evaluation to more complex environments.
References
Please find more details on our approach and experimental results in our research paper. The source code for our model implementation can also be found on our project page.
Article: Overcoming Motion Drifts in Robust Visual Navigation Using Action Adaptive Policy
Visual navigation is crucial for agents operating in novel environments. However, the presence of motion drifts can severely affect an agent's ability to navigate effectively. Motion drifts refer to changes in how an agent's actions influence the environment, which can arise due to various factors such as different flooring materials or obstructions on the robot's wheels.
To address the problem of robust visual navigation in the presence of motion drifts, we propose a novel approach called Action Adaptive Policy (AAP). Our goal is to develop a robust policy that allows agents to adapt to unexpected drifts and improve their navigation performance.
Impact of Motion Drifts on Navigation
Motion drifts have a significant impact on an agent's navigation capabilities. For example, an agent trained to navigate on carpets may struggle to move robustly on bamboo floors. Moreover, when the robot's wheel is obstructed by tape, dust, or hair, the outcomes of actions become even more severely disturbed. To overcome these challenges, it is essential for agents to encode the impact of their actions and adapt accordingly.
Learning a Robust Policy for Navigation
To train our agent's policy, we consider two tasks: point navigation and object navigation. In the point navigation task, the agent's objective is to reach a specific location in the environment. In the object navigation task, the agent needs to locate and navigate to a specific object. We sample movement and rotation drifts at the beginning of each episode to simulate unexpected conditions that the agent may encounter during navigation.
Encoding Action Impact in Navigation
To address motion drifts, we propose to implicitly encode the actual impact of actions on the environment. Rather than focusing on the semantics of actions, we want the agent to learn the expected outcome of its actions based on their observed impact. We design the agent's action embedding in such a way that each dimension represents the observed impact of an action. By recording the outcome of each action in the corresponding dimension of the embedding, the agent can make informed action decisions based on the expected impacts.
Overcoming Fixed Mapping Issue with Transformer Model
While encoding action impact is a promising approach, traditional linear actor models have a fixed mapping between actions and their impact. This limitation prevents agents from adapting to different drifts effectively. To overcome this issue, we utilize a Transformer model as our actor. The Transformer model breaks the fixed mapping problem and requires the agent to choose actions solely based on their impacts.
Experimental Evaluation and Results
We conducted experiments in three different environments: RoboTHOR, Habitat, and Petting Zoo. Our AAP consistently outperformed baseline models in both the point navigation and object navigation tasks in all three environments. The agent's ability to adapt to different drifts, even in extreme cases, was clearly demonstrated.
Qualitative Results and Analysis
We provide qualitative results to showcase the effectiveness and understanding of our AAP model in navigation tasks. The agent demonstrates intelligent decision-making, avoiding obstacles, and successfully reaching target locations or objects. Even with significant drifts, the agent adapts its actions and navigates smoothly to achieve the task objectives.
Conclusion
In this work, we addressed the problem of robust visual navigation by incorporating the observed impact of actions to overcome motion drifts. Our proposed Action Adaptive Policy (AAP) with a Transformer model demonstrated superior performance compared to baseline models in various environments. The agent's adaptability and understanding of the environment were highlighted through qualitative analysis. In the future, further improvements and extensions to more complex environments will be explored.
Highlights
- Proposed an Action Adaptive Policy (AAP) to address the problem of robust visual navigation in the presence of motion drifts.
- Developed an approach to implicitly encode the actual impact of actions on the environment, allowing agents to adapt based on the expected outcomes.
- Utilized a Transformer model as the actor in the AAP, breaking the fixed mapping issue and improving adaptability.
- Conducted experiments in three different environments and consistently outperformed baseline models in both point navigation and object navigation tasks.
- Provided qualitative results showcasing the agent's understanding and adaptability in various navigation tasks.
FAQ
Q: What are motion drifts in visual navigation?
A: Motion drifts refer to changes in how an agent's actions influence the environment during navigation. These drifts can arise due to various factors such as different flooring materials or obstructions on the robot's wheels.
Q: How does the Action Adaptive Policy (AAP) overcome motion drifts?
A: The AAP addresses motion drifts by implicitly encoding the impact of actions on the environment. This allows agents to adapt based on the observed impact and make informed action decisions to navigate effectively in the presence of drifts.
Q: What is the role of the Transformer model in the AAP?
A: The Transformer model is used as the actor in the AAP. It breaks the fixed mapping issue between actions and their impact, requiring agents to choose actions solely based on their expected impacts. This improves adaptability and robustness in navigating with motion drifts.
Q: How does the AAP perform compared to baseline models?
A: Experimental evaluations in multiple environments demonstrate that the AAP consistently outperforms baseline models in both point navigation and object navigation tasks. The agent's adaptability and navigation performance were significantly improved.
Q: How does the agent understand and adapt to different drifts in navigation tasks?
A: By implicitly encoding the actual impact of actions, the agent learns to adapt based on the expected outcomes. Qualitative results showcase the agent's intelligent decision-making and successful navigation in various scenarios, even with significant drifts.
