Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Solowjow, Eugen"'
A robotic behavior model that can reliably generate behaviors from natural language inputs in real time would substantially expedite the adoption of industrial robots due to enhanced system flexibility. To facilitate these efforts, we construct a fra
Externí odkaz:
http://arxiv.org/abs/2309.14894
Autor:
Adebola, Simeon, Parikh, Rishi, Presten, Mark, Sharma, Satvik, Aeron, Shrey, Rao, Ananth, Mukherjee, Sandeep, Qu, Tomson, Wistrom, Christina, Solowjow, Eugen, Goldberg, Ken
The AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom p
Externí odkaz:
http://arxiv.org/abs/2306.17162
Imitation learning has been applied to a range of robotic tasks, but can struggle when robots encounter edge cases that are not represented in the training data (i.e., distribution shift). Interactive fleet learning (IFL) mitigates distribution shift
Externí odkaz:
http://arxiv.org/abs/2306.15228
Learning-based methods in robotics hold the promise of generalization, but what can be done if a learned policy does not generalize to a new situation? In principle, if an agent can at least evaluate its own success (i.e., with a reward classifier th
Externí odkaz:
http://arxiv.org/abs/2210.15206
Autor:
Agboh, Wisdom C., Sharma, Satvik, Srinivas, Kishore, Parulekar, Mallika, Datta, Gaurav, Qiu, Tianshuang, Ichnowski, Jeffrey, Solowjow, Eugen, Dogar, Mehmet, Goldberg, Ken
We consider a decluttering problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface and must be efficiently transported to a packing box using both single and multi-object grasps. Pr
Externí odkaz:
http://arxiv.org/abs/2210.07420
Autor:
Fu, Letian, Danielczuk, Michael, Balakrishna, Ashwin, Brown, Daniel S., Ichnowski, Jeffrey, Solowjow, Eugen, Goldberg, Ken
While deep learning has enabled significant progress in designing general purpose robot grasping systems, there remain objects which still pose challenges for these systems. Recent work on Exploratory Grasping has formalized the problem of systematic
Externí odkaz:
http://arxiv.org/abs/2111.15002
Autor:
Devgon, Shivin, Ichnowski, Jeffrey, Danielczuk, Michael, Brown, Daniel S., Balakrishna, Ashwin, Joshi, Shirin, Rocha, Eduardo M. C., Solowjow, Eugen, Goldberg, Ken
Publikováno v:
Conference on Automation Science and Engineering (CASE) 2021
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly. Kitting is a critical step as it can decrease downstream processing and handling times and enable lower storage and shipping costs. We presen
Externí odkaz:
http://arxiv.org/abs/2107.05789
Active perception systems maximizing information gain to support both monitoring and decision making have seen considerable application in recent work. In this paper, we propose and demonstrate a method of acquiring and extrapolating information in a
Externí odkaz:
http://arxiv.org/abs/2005.00167
Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects. Reinforcement learning (RL) is a promising approach for learning control
Externí odkaz:
http://arxiv.org/abs/2004.14404
Autor:
Solowjow, Eugen, Ugalde, Ines, Shahapurkar, Yash, Aparicio, Juan, Mahler, Jeff, Satish, Vishal, Goldberg, Ken, Claussen, Heiko
Universal grasping of a diverse range of previously unseen objects from heaps is a grand challenge in e-commerce order fulfillment, manufacturing, and home service robotics. Recently, deep learning based grasping approaches have demonstrated results
Externí odkaz:
http://arxiv.org/abs/2004.10251