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pro vyhledávání: '"A Thomason"'
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. Traditionally, and especially for asymptotically optimal sampling-based
Externí odkaz:
http://arxiv.org/abs/2411.17902
Autor:
Hejabi, Parsa, Rahmati, Elnaz, Ziabari, Alireza S., Golazizian, Preni, Thomason, Jesse, Dehghani, Morteza
Large Language Models (LLMs) have shown impressive capabilities in complex tasks and interactive environments, yet their creativity remains underexplored. This paper introduces a simulation framework utilizing the game Balderdash to evaluate both the
Externí odkaz:
http://arxiv.org/abs/2411.10422
Autor:
Liang, Yuanchu, Kim, Edward, Thomason, Wil, Kingston, Zachary, Kurniawati, Hanna, Kavraki, Lydia E.
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., $\geq15$ steps) remai
Externí odkaz:
http://arxiv.org/abs/2411.07032
Language models for American Sign Language (ASL) could make language technologies substantially more accessible to those who sign. To train models on tasks such as isolated sign recognition (ISR) and ASL-to-English translation, datasets provide annot
Externí odkaz:
http://arxiv.org/abs/2411.03568
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
Symbolic planners can discover a sequence of actions from initial to goal states given expert-defined, domain-specific logical action semantics. Large Language Models (LLMs) can directly generate such sequences, but limitations in reasoning and state
Externí odkaz:
http://arxiv.org/abs/2406.02791