9 papers · 1 filter
SMaRT-Tug: Structured Multi-Agent Reinforcement Learning for Physics-Based Tugboat-Barge Collaborative Manipulation
Junkai Lu, Jiadong Zhao, Jiacheng Zhang +8
Autonomous tugboating is central for automating maritime operations such as port logistics and vessel maneuvering, where multiple tugboats must cooperatively transport/manipulate a…
From Impact to Insight: Dynamics-Aware Proprioceptive Terrain Sensing on Granular Media
Yifeng Zhang, Yue Wu, Jake Futterman +5
Robots that traverse natural terrain must interpret contact forces generated under highly dynamic conditions. However, most terrain characterization approaches rely on quasi-static…
Legged Autonomous Surface Science In Analogue Environments (LASSIE): Making Every Robotic Step Count in Planetary Exploration
Cristina G. Wilson, Marion Nachon, Shipeng Liu +20
The ability to efficiently and effectively explore planetary surfaces is currently limited by the capability of wheeled rovers to traverse challenging terrains, and by pre-programm…
Inverse Resistive Force Theory (I-RFT): Learning granular properties through robot-terrain physical interactions
Shipeng Liu, Feng Xue, Yifeng Zhang +2
For robots to navigate safely and efficiently on soft, granular terrains, it is crucial to gather information about the terrain's mechanical properties, which directly affect locom…
Scout-Rover cooperation: online terrain strength mapping and traversal risk estimation for planetary-analog explorations
Shipeng Liu, J. Diego Caporale, Yifeng Zhang +17
Robot-aided exploration of planetary surfaces is essential for understanding geologic processes, yet many scientifically valuable regions, such as Martian dunes and lunar craters,…
HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward
Yuhong Cao, Yizhuo Wang, Jingsong Liang +4
This work pushes the boundaries of learning-based methods in autonomous robot exploration in terms of environmental scale and exploration efficiency. We present HEADER, an attentio…