Zobrazeno 1 - 10
of 5 279
pro vyhledávání: '"Roncone A"'
Autor:
Briscoe-Martinez, Gilberto, Pasricha, Anuj, Abderezaei, Ava, Chaganti, Santosh, Vajrala, Sarath Chandra, Popuri, Sri Kanth, Roncone, Alessandro
This work explores non-prehensile manipulation (NPM) and whole-body interaction as strategies for enabling robotic manipulators to conduct manipulation tasks despite experiencing locked multi-joint (LMJ) failures. LMJs are critical system faults wher
Externí odkaz:
http://arxiv.org/abs/2410.01102
In the evolving landscape of human-autonomy teaming (HAT), fostering effective collaboration and trust between human and autonomous agents is increasingly important. To explore this, we used the game Overcooked AI to create dynamic teaming scenarios
Externí odkaz:
http://arxiv.org/abs/2409.19139
Autor:
Pasricha, Anuj, Roncone, Alessandro
Motion planning for articulated robots has traditionally been governed by algorithms that operate within manufacturer-defined payload limits. Our empirical analysis of the Franka Emika Panda robot demonstrates that this approach unnecessarily restric
Externí odkaz:
http://arxiv.org/abs/2409.18939
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