Zobrazeno 1 - 10
of 641
pro vyhledávání: '"Veerapaneni P"'
Autor:
Knitter, Oliver, Zhao, Dan, Stokes, James, Ganahl, Martin, Leichenauer, Stefan, Veerapaneni, Shravan
Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum proble
Externí odkaz:
http://arxiv.org/abs/2411.03900
Autor:
Jiang, He, Wang, Yutong, Veerapaneni, Rishi, Duhan, Tanishq, Sartoretti, Guillaume, Li, Jiaoyang
Lifelong Multi-Agent Path Finding (LMAPF) is a variant of MAPF where agents are continually assigned new goals, necessitating frequent re-planning to accommodate these dynamic changes. Recently, this field has embraced learning-based methods, which r
Externí odkaz:
http://arxiv.org/abs/2410.21415
We propose Audio Noise Awareness using Visuals of Indoors for NAVIgation for quieter robot path planning. While humans are naturally aware of the noise they make and its impact on those around them, robots currently lack this awareness. A key challen
Externí odkaz:
http://arxiv.org/abs/2410.18932
Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. Methods that address this typi
Externí odkaz:
http://arxiv.org/abs/2410.01798
Robots often face challenges in domestic environments where visual feedback is ineffective, such as retrieving objects obstructed by occlusions or finding a light switch in the dark. In these cases, utilizing contacts to localize the target object ca
Externí odkaz:
http://arxiv.org/abs/2409.18775
Autor:
Veerapaneni, Rishi, Jakobsson, Arthur, Ren, Kevin, Kim, Samuel, Li, Jiaoyang, Likhachev, Maxim
Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely focused on developing high-performance heuristic search methods. Recently
Externí odkaz:
http://arxiv.org/abs/2409.14491
Although quantum computing holds promise to accelerate a wide range of computational tasks, the quantum simulation of quantum dynamics as originally envisaged by Feynman remains the most promising candidate for achieving quantum advantage. A less exp
Externí odkaz:
http://arxiv.org/abs/2407.08006
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, no. 11, pp. 6947-6954, Nov. 2023
In manipulation tasks like plug insertion or assembly that have low tolerance to errors in pose estimation (errors of the order of 2mm can cause task failure), the utilization of touch/contact modality can aid in accurately localizing the object of i
Externí odkaz:
http://arxiv.org/abs/2406.05522
With the advent of machine learning, there have been several recent attempts to learn effective and generalizable heuristics. Local Heuristic A* (LoHA*) is one recent method that instead of learning the entire heuristic estimate, learns a "local" res
Externí odkaz:
http://arxiv.org/abs/2404.06728
Multi-Robot-Arm Motion Planning (M-RAMP) is a challenging problem featuring complex single-agent planning and multi-agent coordination. Recent advancements in extending the popular Conflict-Based Search (CBS) algorithm have made large strides in solv
Externí odkaz:
http://arxiv.org/abs/2405.01772