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PARTNR: A Benchmark for Planning and Reasoning in Human-Robot Collaboration for Household Tasks (Analysis of LLM-Based Agents)

Q: How can we incorporate learning from human demonstrations and feedback to improve the coordination and error recovery capabilities of LLM-based agents in collaborative tasks?

Incorporating learning from human demonstrations and feedback is crucial for enhancing LLM-based agents' coordination and error recovery in collaborative tasks. This can be achieved through several approaches: Learning from Demonstrations: Imitation Learning: Train LLMs on datasets of successful human-human collaborations in PARTNR tasks. This allows the LLM to learn implicit coordination strategies and task division principles from human behavior. Hierarchical Planning with Human Demonstrations: Utilize human demonstrations to train a higher-level planner that can break down complex tasks into sub-tasks, which can then be tackled by the LLM or by requesting human assistance. Integrating Human Feedback: Reinforcement Learning with Human Feedback: Incorporate human feedback as rewards during training. For instance, positive feedback for successful coordination or efficient task completion can be used to guide the LLM towards desirable collaborative behaviors. Error Detection and Recovery from Human Feedback: Train the LLM to recognize specific types of errors (e.g., task-tracking errors, extraneous actions) based on human feedback. This allows the LLM to learn recovery strategies tailored to different error categories. Interactive Learning and Adaptation: Continuous Learning from Human Partners: Enable the LLM to continuously adapt and refine its collaborative strategies through ongoing interactions with human partners. This can be achieved by incorporating mechanisms for online learning and adaptation within the LLM framework. Personalized Collaboration Models: Develop personalized LLM models that adapt to the specific collaborative styles and preferences of individual human users. This can lead to more natural and efficient human-robot interactions. By combining these techniques, we can create LLM-based agents that are more adept at coordinating with humans, learning from their expertise, and gracefully recovering from errors in collaborative settings.

Q: Could the limitations of LLMs in handling temporal and heterogeneous tasks be addressed by integrating symbolic planning methods with deep learning approaches?

Yes, integrating symbolic planning methods with deep learning approaches holds significant promise for addressing the limitations of LLMs in handling temporal and heterogeneous tasks within PARTNR. Symbolic planning, with its ability to reason about explicit logical relationships and constraints, can complement the strengths of deep learning in handling uncertainty and complex data patterns. Here's how this integration can be beneficial: Temporal Reasoning: Symbolic planners excel at handling temporal constraints, ensuring tasks are executed in the correct sequence. They can be used to generate a high-level plan that adheres to temporal dependencies, while LLMs can focus on lower-level decision-making within each step. Hybrid architectures can combine the strengths of both approaches. For instance, a symbolic planner can generate a temporally consistent plan, while an LLM can refine the plan by grounding it in the current world state and handling uncertainties. Heterogeneous Agent Capabilities: Symbolic representations can effectively model the different capabilities of human and robot agents. This information can be used by the planner to allocate tasks appropriately, ensuring that tasks beyond the robot's capabilities are assigned to the human partner. Task Decomposition and Allocation: Symbolic planners can decompose complex tasks into sub-tasks that can be assigned to the appropriate agent based on their capabilities. LLMs can then be used to execute these sub-tasks, leveraging their ability to learn from data and handle variations. By combining the strengths of symbolic planning and deep learning, we can create more robust and capable agents that can effectively handle the complexities of temporal and heterogeneous collaborative tasks in PARTNR.

核心概念

PARTNR, a new benchmark for evaluating human-robot collaboration in household tasks, reveals that while LLMs show promise in planning, they struggle with coordination, error recovery, and real-world perception, highlighting key areas for improvement in embodied AI.

要約

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks (Research Paper Summary)

Bibliographic Information: Chang, M., Chhablani, G., Clegg, A., Cote, M. D., Desai, R., Hlavac, M., ... & Yang, T. Y. (2024). PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks. arXiv preprint arXiv:2411.00081v1.

Research Objective: This paper introduces PARTNR, a novel benchmark designed to evaluate the ability of embodied AI agents, particularly LLM-based planners, to collaborate with humans on a range of household activities within a simulated environment. The research aims to identify the strengths and limitations of current LLM-based approaches in handling complex, collaborative tasks.

Methodology: The researchers developed PARTNR, a dataset comprising 100,000 natural language instructions paired with corresponding evaluation functions. These instructions, focusing on constraint-free, spatial, temporal, and heterogeneous tasks, were generated at scale using LLMs and grounded in a simulated environment built on the Habitat 3.0 simulator and the HSSD dataset. The researchers evaluated various LLM-based planning approaches, including zero-shot prompting, retrieval-augmented generation, and fine-tuned models, under different conditions such as centralized vs. decentralized planning, partial vs. full observability, and learned vs. oracle robot skills. They also conducted human-in-the-loop evaluations to assess the performance of LLM-controlled agents collaborating with real human partners.

Key Findings: The study revealed that while LLM-based planners demonstrate potential in understanding and planning for complex tasks, they face significant challenges:

Coordination: LLMs struggle with coordination in decentralized settings, often leading to inefficient task execution and extraneous actions.
Error Recovery: LLMs exhibit difficulty in recovering from errors arising from learned skill failures or inaccurate perception, highlighting their reliance on robust low-level skills and precise world models.
Real-World Perception: The performance of LLMs significantly degrades when relying on non-privileged perception modules, indicating a need for improved robustness to errors in object detection and localization.
Human-AI Collaboration: LLM-guided robots, while capable of offloading tasks, were found to decrease human efficiency compared to solo human performance, underscoring the need for more effective coordination strategies in human-AI teams.

Main Conclusions: PARTNR provides a valuable benchmark for evaluating and advancing embodied AI agents, particularly in the context of human-robot collaboration. The findings highlight the limitations of current LLM-based approaches, particularly in coordination, error recovery, and real-world perception, emphasizing key areas for future research in embodied AI.

Significance: This research significantly contributes to the field of embodied AI by introducing a large-scale, diverse, and challenging benchmark for evaluating human-robot collaboration. The findings provide crucial insights into the capabilities and limitations of current LLM-based methods, paving the way for developing more robust and efficient collaborative agents.

Limitations and Future Research: The study primarily focused on simulated environments, and future research should investigate the performance of LLM-based agents in real-world settings. Further exploration of novel approaches for improving LLM-based planning, such as incorporating commonsense reasoning, learning from human demonstrations, and developing more robust perception and error recovery mechanisms, is crucial for advancing the field of human-robot collaboration.

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統計

Humans successfully completed 93% of PARTNR tasks in both single and multi-user settings.
The most successful LLM-based agent achieved a 73% success rate with oracle skills and a 57% success rate with learned skills.
When using non-privileged perception, the LLM success rate dropped to 30%.
In decentralized multi-agent settings, task completion took 1.3 times more steps than single-agent scenarios.
Robots guided by LLMs offloaded up to 60% of sub-tasks from humans.
A fine-tuned 8B LLM model achieved comparable performance to a 70B model while being 8.6 times faster at inference.

引用

"While humans are able to solve 93% of PARTNR tasks, SoTA LLMs can only successfully complete 30% under non-privileged conditions."
"In decentralized multi-agent settings, task completion takes 1.3x more steps than single-agent, due to poor tracking of partner actions, resulting in extraneous actions."
"When LLMs are paired with real humans, they require 1.5x as many steps as two humans collaborating and 1.1x more steps than a single human, underscoring the potential for improvement in these models."

抽出されたキーインサイト

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks

by Matthew Chan... 場所 arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00081.pdf

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks

深掘り質問

How can we incorporate learning from human demonstrations and feedback to improve the coordination and error recovery capabilities of LLM-based agents in collaborative tasks?

Incorporating learning from human demonstrations and feedback is crucial for enhancing LLM-based agents' coordination and error recovery in collaborative tasks. This can be achieved through several approaches:

Learning from Demonstrations:

Imitation Learning: Train LLMs on datasets of successful human-human collaborations in PARTNR tasks. This allows the LLM to learn implicit coordination strategies and task division principles from human behavior.
Hierarchical Planning with Human Demonstrations: Utilize human demonstrations to train a higher-level planner that can break down complex tasks into sub-tasks, which can then be tackled by the LLM or by requesting human assistance.

Integrating Human Feedback:

Reinforcement Learning with Human Feedback:  Incorporate human feedback as rewards during training. For instance, positive feedback for successful coordination or efficient task completion can be used to guide the LLM towards desirable collaborative behaviors.
Error Detection and Recovery from Human Feedback: Train the LLM to recognize specific types of errors (e.g., task-tracking errors, extraneous actions) based on human feedback. This allows the LLM to learn recovery strategies tailored to different error categories.

Interactive Learning and Adaptation:

Continuous Learning from Human Partners: Enable the LLM to continuously adapt and refine its collaborative strategies through ongoing interactions with human partners. This can be achieved by incorporating mechanisms for online learning and adaptation within the LLM framework.
Personalized Collaboration Models: Develop personalized LLM models that adapt to the specific collaborative styles and preferences of individual human users. This can lead to more natural and efficient human-robot interactions.

By combining these techniques, we can create LLM-based agents that are more adept at coordinating with humans, learning from their expertise, and gracefully recovering from errors in collaborative settings.

Could the limitations of LLMs in handling temporal and heterogeneous tasks be addressed by integrating symbolic planning methods with deep learning approaches?

Yes, integrating symbolic planning methods with deep learning approaches holds significant promise for addressing the limitations of LLMs in handling temporal and heterogeneous tasks within PARTNR.

Symbolic planning, with its ability to reason about explicit logical relationships and constraints, can complement the strengths of deep learning in handling uncertainty and complex data patterns.
Here's how this integration can be beneficial:

Temporal Reasoning:

Symbolic planners excel at handling temporal constraints, ensuring tasks are executed in the correct sequence. They can be used to generate a high-level plan that adheres to temporal dependencies, while LLMs can focus on lower-level decision-making within each step.
Hybrid architectures can combine the strengths of both approaches. For instance, a symbolic planner can generate a temporally consistent plan, while an LLM can refine the plan by grounding it in the current world state and handling uncertainties.

Heterogeneous Agent Capabilities:

Symbolic representations can effectively model the different capabilities of human and robot agents. This information can be used by the planner to allocate tasks appropriately, ensuring that tasks beyond the robot's capabilities are assigned to the human partner.
Task Decomposition and Allocation:  Symbolic planners can decompose complex tasks into sub-tasks that can be assigned to the appropriate agent based on their capabilities. LLMs can then be used to execute these sub-tasks, leveraging their ability to learn from data and handle variations.

By combining the strengths of symbolic planning and deep learning, we can create more robust and capable agents that can effectively handle the complexities of temporal and heterogeneous collaborative tasks in PARTNR.

What are the ethical implications of deploying robots capable of collaborating with humans in domestic settings, and how can PARTNR be used to study and address these concerns?

Deploying collaborative robots in homes raises several ethical considerations:

Job Displacement:  As robots become more adept at household tasks, concerns arise about potential job displacement for domestic workers.  It's crucial to consider the socioeconomic impact and ensure a just transition for those affected.
Privacy and Data Security: Robots equipped with sensors for navigation and object recognition will inevitably collect data about the home environment and human behavior.  Safeguarding this data and ensuring user privacy is paramount.
Autonomy and Control:  Determining the appropriate level of robot autonomy in decision-making is crucial. Users should have clear mechanisms for control and oversight, ensuring they feel comfortable and safe in their homes.
Bias and Fairness:  LLMs are trained on massive datasets, which may contain biases that could translate into unfair or discriminatory robot behavior.  It's essential to address these biases during training and ensure robots act fairly and equitably in all situations.
Impact on Human Relationships: The introduction of robots into the home could alter family dynamics and human-human interactions.  It's important to study these potential impacts and design robots that foster positive and supportive relationships.

PARTNR as a tool for studying and addressing ethical concerns:

Controlled Experimentation: PARTNR provides a safe and controlled environment to study human-robot collaboration, allowing researchers to systematically investigate ethical concerns.
Bias Detection and Mitigation:  Researchers can use PARTNR to analyze the behavior of LLM-based agents for potential biases and develop techniques to mitigate unfair or discriminatory outcomes.
User Studies and Feedback: PARTNR's human-in-the-loop evaluation framework enables researchers to conduct user studies, gather feedback on ethical concerns, and incorporate user perspectives into the design and development process.
Transparency and Explainability:  By analyzing the decision-making process of LLM-based agents in PARTNR, researchers can work towards making these systems more transparent and explainable, fostering trust and understanding among users.
By leveraging PARTNR as a research tool, we can proactively address ethical implications, ensuring the responsible and beneficial integration of collaborative robots into domestic settings.