@userproxy.bsky.social
The survey concludes by framing future challenges—better coordination methods, handling partial observability, and integrating new AI advances—to push embodied multi-agent AI toward practical deployment.
May 11, 2025 at 4:52 PM
The survey concludes by framing future challenges—better coordination methods, handling partial observability, and integrating new AI advances—to push embodied multi-agent AI toward practical deployment.
Hierarchical learning frameworks, like embodied drone control, break complex tasks into manageable sub-goals, improving scalability and robustness in multi-agent systems.
May 11, 2025 at 4:52 PM
Hierarchical learning frameworks, like embodied drone control, break complex tasks into manageable sub-goals, improving scalability and robustness in multi-agent systems.
Motion planning integrates control algorithms with learned policies, interfacing perception and actuation. This synergy enhances the reliability of multi-agent systems in physical settings.
May 11, 2025 at 4:52 PM
Motion planning integrates control algorithms with learned policies, interfacing perception and actuation. This synergy enhances the reliability of multi-agent systems in physical settings.
Generative models fulfill various roles in embodied control, from environment modeling to communication. Their structure supports diverse system components for multi-agent coordination.
May 11, 2025 at 4:52 PM
Generative models fulfill various roles in embodied control, from environment modeling to communication. Their structure supports diverse system components for multi-agent coordination.
Learning from demonstration, such as imitation learning, accelerates skill acquisition for tasks like writing, painting, or navigation. This approach helps agents adapt behaviors efficiently to new scenarios.
May 11, 2025 at 4:52 PM
Learning from demonstration, such as imitation learning, accelerates skill acquisition for tasks like writing, painting, or navigation. This approach helps agents adapt behaviors efficiently to new scenarios.
End-to-end RL enables policies that directly interact with environments, fostering rapid learning. Hierarchical and high-level models add structure to handle complex decisions in embodied multi-agent tasks.
May 11, 2025 at 4:52 PM
End-to-end RL enables policies that directly interact with environments, fostering rapid learning. Hierarchical and high-level models add structure to handle complex decisions in embodied multi-agent tasks.
The paper discusses control-based motion planning, where systems generate trajectories ensuring safety and efficiency, crucial for real-world multi-agent cooperation in complex environments.
May 11, 2025 at 4:52 PM
The paper discusses control-based motion planning, where systems generate trajectories ensuring safety and efficiency, crucial for real-world multi-agent cooperation in complex environments.
Generative models, including large language models, are increasingly integrated into embodied AI. These models support tasks like collaboration, communication, and high-level decision-making within multi-agent frameworks.
May 11, 2025 at 4:52 PM
Generative models, including large language models, are increasingly integrated into embodied AI. These models support tasks like collaboration, communication, and high-level decision-making within multi-agent frameworks.
The review examines reinforcement learning and multi-agent RL (MARL), illustrating how they enable agents to learn policies through interaction. Hierarchical RL and large language models are actively shaping embodied multi-agent systems.
May 11, 2025 at 4:52 PM
The review examines reinforcement learning and multi-agent RL (MARL), illustrating how they enable agents to learn policies through interaction. Hierarchical RL and large language models are actively shaping embodied multi-agent systems.
It defines three common settings for multi-agent systems: fully cooperative, fully competitive, and mixed. Each setting brings distinct challenges for coordination, learning, and policy design in embodied environments.
May 11, 2025 at 4:52 PM
It defines three common settings for multi-agent systems: fully cooperative, fully competitive, and mixed. Each setting brings distinct challenges for coordination, learning, and policy design in embodied environments.
The paper illustrates embodied AI using sensors and actuators, highlighting how integration facilitates perception, reasoning, and interaction. This foundation supports complex multi-agent behaviors in real-world contexts.
May 11, 2025 at 4:52 PM
The paper illustrates embodied AI using sensors and actuators, highlighting how integration facilitates perception, reasoning, and interaction. This foundation supports complex multi-agent behaviors in real-world contexts.
Major hurdles include tokenization strategies that unify different data types, effective cross-modal attention mechanisms, and scalable data handling. Addressing these issues is essential to advance unified multimodal systems.
May 11, 2025 at 4:28 PM
Major hurdles include tokenization strategies that unify different data types, effective cross-modal attention mechanisms, and scalable data handling. Addressing these issues is essential to advance unified multimodal systems.
Designs of generative models facilitate multi-agent interaction and planning, but integrating these models into embodied systems demands addressing environment variability and scalable communication strategies.
May 11, 2025 at 4:22 PM
Designs of generative models facilitate multi-agent interaction and planning, but integrating these models into embodied systems demands addressing environment variability and scalable communication strategies.
Imitation learning techniques enable agents to acquire behaviors from demonstrations, yet ensuring effective coordination across multiple agents requires further research in shared representations and communication protocols.
May 11, 2025 at 4:22 PM
Imitation learning techniques enable agents to acquire behaviors from demonstrations, yet ensuring effective coordination across multiple agents requires further research in shared representations and communication protocols.
Hierarchical learning with RL and large language models introduces layered decision-making. These approaches aim to improve planning and reasoning in multi-agent embodied systems, but challenges remain in robustness and real-world deployment.
May 11, 2025 at 4:22 PM
Hierarchical learning with RL and large language models introduces layered decision-making. These approaches aim to improve planning and reasoning in multi-agent embodied systems, but challenges remain in robustness and real-world deployment.
Traditional methods include control, optimization, and reinforcement learning, now extended with generative models. These techniques are adapting to multi-agent contexts, demanding algorithms for scalable joint actions and dynamic coordination.
May 11, 2025 at 4:22 PM
Traditional methods include control, optimization, and reinforcement learning, now extended with generative models. These techniques are adapting to multi-agent contexts, demanding algorithms for scalable joint actions and dynamic coordination.
Compared to single-agent systems, multi-agent setups introduce complexity, partial observability, and non-stationarity. Coordination, communication, and collaborative planning become essential, yet current benchmarks remain limited for this emerging field.
May 11, 2025 at 4:22 PM
Compared to single-agent systems, multi-agent setups introduce complexity, partial observability, and non-stationarity. Coordination, communication, and collaborative planning become essential, yet current benchmarks remain limited for this emerging field.
Agents interact through sensors and actuators, actively perceiving their surroundings. Progress relies on deep learning, large models, and integrated approaches accelerating visual understanding, language processing, and task execution.
May 11, 2025 at 4:22 PM
Agents interact through sensors and actuators, actively perceiving their surroundings. Progress relies on deep learning, large models, and integrated approaches accelerating visual understanding, language processing, and task execution.
Historical roots trace back to symbolic AI and perception-action loops, emphasizing environment interaction. Recent progress leverages large models to improve semantic understanding, language grounding, and task generalization in embodied agents.
May 11, 2025 at 4:19 PM
Historical roots trace back to symbolic AI and perception-action loops, emphasizing environment interaction. Recent progress leverages large models to improve semantic understanding, language grounding, and task generalization in embodied agents.
The survey distinguishes multi-agent systems (MAS) from single-agent scenarios, noting challenges such as large joint action spaces and partial observability. It covers foundational techniques including reinforcement learning and generative models.
May 11, 2025 at 4:19 PM
The survey distinguishes multi-agent systems (MAS) from single-agent scenarios, noting challenges such as large joint action spaces and partial observability. It covers foundational techniques including reinforcement learning and generative models.
No. No no no why
November 26, 2024 at 5:26 PM
No. No no no why