Zobrazeno 1 - 10
of 12 494
pro vyhledávání: '"Thomason, A"'
Decentralized Learning (DL) enables privacy-preserving collaboration among organizations or users to enhance the performance of local deep learning models. However, model aggregation becomes challenging when client data is heterogeneous, and identify
Externí odkaz:
http://arxiv.org/abs/2409.10720
Navigating unfamiliar environments presents significant challenges for blind and low-vision (BLV) individuals. In this work, we construct a dataset of images and goals across different scenarios such as searching through kitchens or navigating outdoo
Externí odkaz:
http://arxiv.org/abs/2407.08219
Robot evaluations in language-guided, real world settings are time-consuming and often sample only a small space of potential instructions across complex scenes. In this work, we introduce contrast sets for robotics as an approach to make small, but
Externí odkaz:
http://arxiv.org/abs/2406.13636
This paper studies in-context learning by decomposing the output of large language models into the individual contributions of attention heads and MLPs (components). We observe curious components: good-performing ones that individually do well on a c
Externí odkaz:
http://arxiv.org/abs/2406.13131
Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems such as manipulators, the most time-intensive component is collision c
Externí odkaz:
http://arxiv.org/abs/2406.02807
Classical planning approaches guarantee finding a set of actions that can achieve a given goal state when possible, but require an expert to specify logical action semantics that govern the dynamics of the environment. Researchers have shown that Lar
Externí odkaz:
http://arxiv.org/abs/2406.02791
Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture tempo
Externí odkaz:
http://arxiv.org/abs/2403.17246
Training robots to perform complex control tasks from high-dimensional pixel input using reinforcement learning (RL) is sample-inefficient, because image observations are comprised primarily of task-irrelevant information. By contrast, humans are abl
Externí odkaz:
http://arxiv.org/abs/2403.10940
Autor:
Srinivasan, Tejas, Hessel, Jack, Gupta, Tanmay, Lin, Bill Yuchen, Choi, Yejin, Thomason, Jesse, Chandu, Khyathi Raghavi
Selective prediction minimizes incorrect predictions from vision-language models (VLMs) by allowing them to abstain from answering when uncertain. However, when deploying a vision-language system with low tolerance for inaccurate predictions, selecti
Externí odkaz:
http://arxiv.org/abs/2402.15610
Humans perceive and comprehend different visual properties of an object based on specific contexts. For instance, we know that a banana turns brown ``when it becomes rotten,'' whereas it appears green ``when it is unripe.'' Previous studies on probin
Externí odkaz:
http://arxiv.org/abs/2402.13584