Improving Embodied Agents with Context-Aware Planning and Environment-Aware Memory

Alapfogalmak

Enhancing embodied agents with context-aware planning and environment-aware memory improves navigation and object interaction efficiency.

Kivonat

The article discusses the challenges faced by embodied agents in navigating environments and interacting with objects. It introduces CAPEAM, a system that incorporates context-aware planning (CAP) and environment-aware memory (EAM) to address these challenges. CAP focuses on predicting task-relevant objects, while EAM stores information about object states for better navigation. The proposed system aims to improve agent performance in interactive instruction following tasks by considering consequences of actions and spatial arrangements of objects. Empirical results show significant performance improvements in various metrics, especially in unseen environments. The article also includes an ablation study to demonstrate the individual benefits of CAP and EAM components.

Statisztikák

We empirically show that the agent with the proposed CAPEAM achieves state-of-the-art performance in various metrics using a challenging interactive instruction following benchmark in both seen and unseen environments by large margins (up to +10.70% in unseen env.)
For training and evaluation, we use the widely used challenging benchmark for interactive instruction following [36].
Our method outperforms existing methods, especially in unseen environments even without human-designed plan templates.
We observe significant performance drops (-5.35% and -4.45% in SR) in both seen and unseen environments when EAM is ablated from our agent.
Using both CAP and EAM improves our agent more than using either of them.

Idézetek

"We propose CAPEAM that incorporates context in planning and remembers environmental changes for embodied AI agents."
"Our method outperforms existing methods, especially in unseen environments even without human-designed plan templates."
"The proposed system aims to improve agent performance in interactive instruction following tasks by considering consequences of actions."

Főbb Kivonatok

Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents

by Byeonghwi Ki... : arxiv.org 03-14-2024

https://arxiv.org/pdf/2308.07241.pdf

Mélyebb kérdések

How can the concept of context be dynamically adjusted during task execution for improved adaptability

To dynamically adjust the concept of context during task execution for improved adaptability, the system can incorporate feedback loops that continuously monitor the environment and task progress. By integrating sensors or cameras to gather real-time data, the system can update its understanding of the context based on changing conditions. Machine learning algorithms can be employed to analyze this data and make adjustments to the planned actions accordingly. Additionally, reinforcement learning techniques can enable the system to learn from its interactions and modify its behavior in response to new information. This adaptive approach allows for flexibility in responding to unexpected changes or variations in the environment.

What are potential limitations or drawbacks of relying on fixed context predictions for planning

Relying solely on fixed context predictions for planning may have limitations when faced with dynamic environments or tasks that involve unpredictable elements. One potential drawback is that fixed contexts may not account for variations or exceptions that arise during task execution, leading to suboptimal performance or failure in completing tasks successfully. Moreover, if there are inaccuracies in initial context predictions, it could result in inefficient use of resources and time as well as errors in action sequences. The lack of adaptability inherent in fixed contexts may hinder overall system performance when dealing with complex and evolving scenarios.

How can the integration of additional sensory modalities enhance the effectiveness of context-aware planning and environment-aware memory systems

The integration of additional sensory modalities can significantly enhance the effectiveness of context-aware planning and environment-aware memory systems by providing a more comprehensive understanding of the surroundings. For example:

Depth Sensors: Depth information can help create more accurate spatial representations, aiding navigation and object interaction.
Audio Input: Audio cues from microphones can provide valuable contextual information about surrounding sounds or verbal instructions.
Tactile Feedback: Incorporating touch sensors or haptic feedback mechanisms enables better interaction with physical objects.
Multi-Camera Systems: Utilizing multiple cameras allows for a broader field of view and depth perception, enhancing environmental awareness.
By combining these sensory inputs with existing visual data, machine learning models can generate richer contextual insights for improved decision-making processes within embodied agents' tasks.