Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Sferrazza, Carmelo"'
Autor:
Singh, Himanshu Gaurav, Loquercio, Antonio, Sferrazza, Carmelo, Wu, Jane, Qi, Haozhi, Abbeel, Pieter, Malik, Jitendra
We present an approach to learn general robot manipulation priors from 3D hand-object interaction trajectories. We build a framework to use in-the-wild videos to generate sensorimotor robot trajectories. We do so by lifting both the human hand and th
Externí odkaz:
http://arxiv.org/abs/2409.08273
In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision. Despite notable evidence of successful deployment of this architecture in the c
Externí odkaz:
http://arxiv.org/abs/2408.06316
Humanoid robots hold great promise in assisting humans in diverse environments and tasks, due to their flexibility and adaptability leveraging human-like morphology. However, research in humanoid robots is often bottlenecked by the costly and fragile
Externí odkaz:
http://arxiv.org/abs/2403.10506
Distributed tactile sensing for multi-force detection is crucial for various aerial robot interaction tasks. However, current contact sensing solutions on drones only exploit single end-effector sensors and cannot provide distributed multi-contact se
Externí odkaz:
http://arxiv.org/abs/2401.17149
Humans rely on the synergy of their senses for most essential tasks. For tasks requiring object manipulation, we seamlessly and effectively exploit the complementarity of our senses of vision and touch. This paper draws inspiration from such capabili
Externí odkaz:
http://arxiv.org/abs/2311.00924
Autor:
Adeniji, Ademi, Xie, Amber, Sferrazza, Carmelo, Seo, Younggyo, James, Stephen, Abbeel, Pieter
Using learned reward functions (LRFs) as a means to solve sparse-reward reinforcement learning (RL) tasks has yielded some steady progress in task-complexity through the years. In this work, we question whether today's LRFs are best-suited as a direc
Externí odkaz:
http://arxiv.org/abs/2308.12270
Learning from human preferences is important for language models to match human needs and to align with human and social values. Prior works have achieved remarkable successes by learning from human feedback to understand and follow instructions. Non
Externí odkaz:
http://arxiv.org/abs/2302.02676
Grasping objects whose physical properties are unknown is still a great challenge in robotics. Most solutions rely entirely on visual data to plan the best grasping strategy. However, to match human abilities and be able to reliably pick and hold unk
Externí odkaz:
http://arxiv.org/abs/2109.11504
This paper aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the objects to be manipulated. For this purpose, a robotic system is pres
Externí odkaz:
http://arxiv.org/abs/2101.02680
The images captured by vision-based tactile sensors carry information about high-resolution tactile fields, such as the distribution of the contact forces applied to their soft sensing surface. However, extracting the information encoded in the image
Externí odkaz:
http://arxiv.org/abs/2012.11295