Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Bucher, Bernadette"'
We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision. This is enabled by 1) task-relevant autonomy, which guides exploration towards object intera
Externí odkaz:
http://arxiv.org/abs/2409.20568
Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to new environment conditions remains a major challenge. Toward addressing
Externí odkaz:
http://arxiv.org/abs/2403.18222
Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like search behaviors. We introduce a zero-shot navigation approach, Vision-La
Externí odkaz:
http://arxiv.org/abs/2312.03275
Autor:
Cai, Xiaoyi, Ancha, Siddharth, Sharma, Lakshay, Osteen, Philip R., Bucher, Bernadette, Phillips, Stephen, Wang, Jiuguang, Everett, Michael, Roy, Nicholas, How, Jonathan P.
Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terrain properties directly from data via self-supervision to automatically
Externí odkaz:
http://arxiv.org/abs/2311.06234
Autor:
Georgakis, Georgios, Bucher, Bernadette, Arapin, Anton, Schmeckpeper, Karl, Matni, Nikolai, Daniilidis, Kostas
We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging. We argue that learning occupancy priors o
Externí odkaz:
http://arxiv.org/abs/2202.11907
Autor:
Ebert, Frederik, Yang, Yanlai, Schmeckpeper, Karl, Bucher, Bernadette, Georgakis, Georgios, Daniilidis, Kostas, Finn, Chelsea, Levine, Sergey
Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which can be prohibitively expensive to collect. In other fields, such as comp
Externí odkaz:
http://arxiv.org/abs/2109.13396
Autor:
Georgakis, Georgios, Bucher, Bernadette, Schmeckpeper, Karl, Singh, Siddharth, Daniilidis, Kostas
We consider the problem of object goal navigation in unseen environments. Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the spatial and semantic variability of indoor environments. Current methods
Externí odkaz:
http://arxiv.org/abs/2106.15648
Autor:
Shaik, Sadat, Bucher, Bernadette, Agrafiotis, Nephele, Phillips, Stephen, Daniilidis, Kostas, Schmenner, William
Style analysis of artwork in computer vision predominantly focuses on achieving results in target image generation through optimizing understanding of low level style characteristics such as brush strokes. However, fundamentally different techniques
Externí odkaz:
http://arxiv.org/abs/2012.04153
Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature. Existing model-based curiosity methods look to approximate prediction unce
Externí odkaz:
http://arxiv.org/abs/2003.06082
Autor:
Dasari, Sudeep, Ebert, Frederik, Tian, Stephen, Nair, Suraj, Bucher, Bernadette, Schmeckpeper, Karl, Singh, Siddharth, Levine, Sergey, Finn, Chelsea
Robot learning has emerged as a promising tool for taming the complexity and diversity of the real world. Methods based on high-capacity models, such as deep networks, hold the promise of providing effective generalization to a wide range of open-wor
Externí odkaz:
http://arxiv.org/abs/1910.11215