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pro vyhledávání: '"Roncone A"'
In this work, we present a novel algorithm to perform spill-free handling of open-top liquid-filled containers that operates in cluttered environments. By allowing liquid-filled containers to be tilted at higher angles and enabling motion along all a
Externí odkaz:
http://arxiv.org/abs/2408.00215
Autor:
Hulle, Nikhil, Aroca-Ouellette, Stéphane, Ries, Anthony J., Brawer, Jake, von der Wense, Katharina, Roncone, Alessandro
Effective collaboration between humans and AIs hinges on transparent communication and alignment of mental models. However, explicit, verbal communication is not always feasible. Under such circumstances, human-human teams often depend on implicit, n
Externí odkaz:
http://arxiv.org/abs/2407.03298
Publikováno v:
Proceedings of the 2024 American Control Conference (ACC), 2024
Effective multi-agent collaboration is imperative for solving complex, distributed problems. In this context, two key challenges must be addressed: first, autonomously identifying optimal objectives for collective outcomes; second, aligning these obj
Externí odkaz:
http://arxiv.org/abs/2404.03984
Autor:
Pasricha, Anuj, Roncone, Alessandro
In this work, we introduce LazyBoE, a multi-query method for kinodynamic motion planning with forward propagation. This algorithm allows for the simultaneous exploration of a robot's state and control spaces, thereby enabling a wider suite of dynamic
Externí odkaz:
http://arxiv.org/abs/2403.07867
Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently
Externí odkaz:
http://arxiv.org/abs/2401.12965
For safe and effective operation of humanoid robots in human-populated environments, the problem of commanding a large number of Degrees of Freedom (DoF) while simultaneously considering dynamic obstacles and human proximity has still not been solved
Externí odkaz:
http://arxiv.org/abs/2312.02711
Understanding the intentions of human teammates is critical for safe and effective human-robot interaction. The canonical approach for human-aware robot motion planning is to first predict the human's goal or path, and then construct a robot plan tha
Externí odkaz:
http://arxiv.org/abs/2311.05562
Task assignment and scheduling algorithms are powerful tools for autonomously coordinating large teams of robotic or AI agents. However, the decisions these system make often rely on components designed by domain experts, which can be difficult for n
Externí odkaz:
http://arxiv.org/abs/2311.00153
Publikováno v:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
Current motion planning approaches rely on binary collision checking to evaluate the validity of a state and thereby dictate where the robot is allowed to move. This approach leaves little room for robots to engage in contact with an object, as is of
Externí odkaz:
http://arxiv.org/abs/2310.06210
Kitting refers to the task of preparing and grouping necessary parts and tools (or "kits") for assembly in a manufacturing environment. Automating this process simplifies the assembly task for human workers and improves efficiency. Existing automated
Externí odkaz:
http://arxiv.org/abs/2209.08387