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New CALM system uses multi-agent RL for cooperative aerial manipulation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CALM (Cooperative Aerial Learned Manipulation), a novel decentralized controller designed for multiple unmanned aerial vehicles (UAVs) to cooperatively manipulate a shared payload. This system utilizes Multi-Agent Reinforcement Learning (MARL), specifically Multi-Agent Proximal Policy Optimization (MAPPO), combined with extended state observers and anticipatory yaw control to mitigate issues like wind and noisy goal broadcasts. CALM demonstrated superior performance in reducing position and orientation errors compared to various baselines in simulations. AI

IMPACT This research could advance the capabilities of drone swarms in complex, dynamic environments, potentially impacting logistics and construction.

RANK_REASON The cluster contains an academic paper detailing a new method for multi-agent reinforcement learning in robotics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CALM system uses multi-agent RL for cooperative aerial manipulation

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The cluster contains an academic paper detailing a new method for multi-agent reinforcement learning in robotics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Fausto Francesco Lizzio ·

    CALM: Decentralized Control of Cooperative Aerial Manipulation with Multi-Agent Reinforcement Learning

    Cooperative aerial manipulation is difficult under wind, payload motion, and noisy goal broadcasts because multiple Unmanned Aerial Vehicles (UAVs) are coupled through a shared payload. We present CALM (Cooperative Aerial Learned Manipulation), a decentralized controller for aeri…

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