MARS-Dragonfly creates a force-torque virtual quadrotor model and two-stage predictive allocator that lets reconfigurable drone modules fly stably and agilely, validated in real-world experiments with 0.0896 m average position error.
Decentralized aerial manipulation of a cable-suspended load using multi-agent reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Aco2 trains a quadrotor policy in simulation that adapts to diverse payload dynamics via latent context encoding and contrastive structuring, enabling zero-shot real-world deployment for autonomous aerial delivery.
citing papers explorer
-
MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems
MARS-Dragonfly creates a force-torque virtual quadrotor model and two-stage predictive allocator that lets reconfigurable drone modules fly stably and agilely, validated in real-world experiments with 0.0896 m average position error.
-
Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
Aco2 trains a quadrotor policy in simulation that adapts to diverse payload dynamics via latent context encoding and contrastive structuring, enabling zero-shot real-world deployment for autonomous aerial delivery.