Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Talbot, Ben"'
Autor:
Deitke, Matt, Batra, Dhruv, Bisk, Yonatan, Campari, Tommaso, Chang, Angel X., Chaplot, Devendra Singh, Chen, Changan, D'Arpino, Claudia Pérez, Ehsani, Kiana, Farhadi, Ali, Fei-Fei, Li, Francis, Anthony, Gan, Chuang, Grauman, Kristen, Hall, David, Han, Winson, Jain, Unnat, Kembhavi, Aniruddha, Krantz, Jacob, Lee, Stefan, Li, Chengshu, Majumder, Sagnik, Maksymets, Oleksandr, Martín-Martín, Roberto, Mottaghi, Roozbeh, Raychaudhuri, Sonia, Roberts, Mike, Savarese, Silvio, Savva, Manolis, Shridhar, Mohit, Sünderhauf, Niko, Szot, Andrew, Talbot, Ben, Tenenbaum, Joshua B., Thomason, Jesse, Toshev, Alexander, Truong, Joanne, Weihs, Luca, Wu, Jiajun
We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) em
Externí odkaz:
http://arxiv.org/abs/2210.06849
Autor:
Rana, Krishan, Dasagi, Vibhavari, Haviland, Jesse, Talbot, Ben, Milford, MIchael, Sünderhauf, Niko
While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environ
Externí odkaz:
http://arxiv.org/abs/2112.05299
Recent Semantic SLAM methods combine classical geometry-based estimation with deep learning-based object detection or semantic segmentation. In this paper we evaluate the quality of semantic maps generated by state-of-the-art class- and instance-awar
Externí odkaz:
http://arxiv.org/abs/2109.07748
Autor:
Rana, Krishan, Dasagi, Vibhavari, Haviland, Jesse, Talbot, Ben, Milford, Michael, Sünderhauf, Niko
We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learning (RL). BCF thrives in the robotics domain, where reliable but subopti
Externí odkaz:
http://arxiv.org/abs/2107.09822
Domestic and service robots have the potential to transform industries such as health care and small-scale manufacturing, as well as the homes in which we live. However, due to the overwhelming variety of tasks these robots will be expected to comple
Externí odkaz:
http://arxiv.org/abs/2106.01650
Autor:
Hall, David, Talbot, Ben, Bista, Suman Raj, Zhang, Haoyang, Smith, Rohan, Dayoub, Feras, Sünderhauf, Niko
Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. However, it is difficult to evaluate progress in this area due to a lac
Externí odkaz:
http://arxiv.org/abs/2009.05246
Autor:
Talbot, Ben, Hall, David, Zhang, Haoyang, Bista, Suman Raj, Smith, Rohan, Dayoub, Feras, Sünderhauf, Niko
We introduce BenchBot, a novel software suite for benchmarking the performance of robotics research across both photorealistic 3D simulations and real robot platforms. BenchBot provides a simple interface to the sensorimotor capabilities of a robot w
Externí odkaz:
http://arxiv.org/abs/2008.00635
Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is
Externí odkaz:
http://arxiv.org/abs/2003.05117
Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as labels, signs, maps, planners, spoken directions, and navigational ges
Externí odkaz:
http://arxiv.org/abs/2001.11684
In this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones. We present an extension of the residual reinforcement learning framework f
Externí odkaz:
http://arxiv.org/abs/1909.10972