Zobrazeno 1 - 10
of 6 700
pro vyhledávání: '"A. Nishanth"'
In this work, we use a Discrete Element Method (DEM) to explore the viscous to inertial shear thickening transition of dense frictionless non-Brownian suspensions close to jamming. This transition is characterized by a change in the steady state rheo
Externí odkaz:
http://arxiv.org/abs/2410.12140
Autor:
Wang, Zeqiang, Wu, Jiageng, Wang, Yuqi, Wang, Wei, Yang, Jie, Johnson, Jon, Sastry, Nishanth, De, Suparna
Social media is recognized as an important source for deriving insights into public opinion dynamics and social impacts due to the vast textual data generated daily and the 'unconstrained' behavior of people interacting on these platforms. However, s
Externí odkaz:
http://arxiv.org/abs/2410.08352
India's urbanization is often characterized as particularly challenging and very unequal but systematic empirical analyses, comparable to other nations, have largely been lacking. Here, we characterize India's economic and human development along wit
Externí odkaz:
http://arxiv.org/abs/2410.04737
Autor:
Duan, Jiafei, Pumacay, Wilbert, Kumar, Nishanth, Wang, Yi Ru, Tian, Shulin, Yuan, Wentao, Krishna, Ranjay, Fox, Dieter, Mandlekar, Ajay, Guo, Yijie
Robotic manipulation in open-world settings requires not only task execution but also the ability to detect and learn from failures. While recent advances in vision-language models (VLMs) and large language models (LLMs) have improved robots' spatial
Externí odkaz:
http://arxiv.org/abs/2410.00371
Autor:
Agarwal, Vibhor, Jin, Yiqiao, Chandra, Mohit, De Choudhury, Munmun, Kumar, Srijan, Sastry, Nishanth
The remarkable capabilities of large language models (LLMs) in language understanding and generation have not rendered them immune to hallucinations. LLMs can still generate plausible-sounding but factually incorrect or fabricated information. As LLM
Externí odkaz:
http://arxiv.org/abs/2409.19492
The real world is unpredictable. Therefore, to solve long-horizon decision-making problems with autonomous robots, we must construct agents that are capable of adapting to changes in the environment during deployment. Model-based planning approaches
Externí odkaz:
http://arxiv.org/abs/2409.19226
Autor:
Vodrahalli, Kiran, Ontanon, Santiago, Tripuraneni, Nilesh, Xu, Kelvin, Jain, Sanil, Shivanna, Rakesh, Hui, Jeffrey, Dikkala, Nishanth, Kazemi, Mehran, Fatemi, Bahare, Anil, Rohan, Dyer, Ethan, Shakeri, Siamak, Vij, Roopali, Mehta, Harsh, Ramasesh, Vinay, Le, Quoc, Chi, Ed, Lu, Yifeng, Firat, Orhan, Lazaridou, Angeliki, Lespiau, Jean-Baptiste, Attaluri, Nithya, Olszewska, Kate
We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbit
Externí odkaz:
http://arxiv.org/abs/2409.12640
Tracking controllers enable robotic systems to accurately follow planned reference trajectories. In particular, reinforcement learning (RL) has shown promise in the synthesis of controllers for systems with complex dynamics and modest online compute
Externí odkaz:
http://arxiv.org/abs/2409.11238
Causal language modeling using the Transformer architecture has yielded remarkable capabilities in Large Language Models (LLMs) over the last few years. However, the extent to which fundamental search and reasoning capabilities emerged within LLMs re
Externí odkaz:
http://arxiv.org/abs/2409.10502
Autor:
Peng, Andi, Li, Belinda Z., Sucholutsky, Ilia, Kumar, Nishanth, Shah, Julie A., Andreas, Jacob, Bobu, Andreea
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations. To learn a good reward, it is necessary to determine which features of the environment are relevant before determining how these features shoul
Externí odkaz:
http://arxiv.org/abs/2409.08212