Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Mrinal Kalakrishnan"'
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e47299376b1f1149d79eb0b685dc453d
Publikováno v:
Robotics: Science and Systems
The distributional perspective on reinforcement learning (RL) has given rise to a series of successful Q-learning algorithms, resulting in state-of-the-art performance in arcade game environments. However, it has not yet been analyzed how these findi
Publikováno v:
ICRA
This paper introduces Action Image, a new grasp proposal representation that allows learning an end-to-end deep-grasping policy. Our model achieves $84\%$ grasp success on $172$ real world objects while being trained only in simulation on $48$ object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1536e962b6c0a03ff993e660780d8acc
Publikováno v:
ICRA
Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time to infer the best action from the value network. As a result, the infe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8d22d2e801bbd3b11a841c76d5b7784
http://arxiv.org/abs/1904.07319
http://arxiv.org/abs/1904.07319
Publikováno v:
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
In principle, reinforcement learning and policy search methods can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generaliz
Publikováno v:
ICRA
We present a policy search method for learning complex feedback control policies that map from high-dimensional sensory inputs to motor torques, for manipulation tasks with discontinuous contact dynamics. We build on a prior technique called guided p
Publikováno v:
ACC
We develop TROSS, a solver for constrained trajectory optimization based on a sequential operator splitting framework. TROSS iteratively improves trajectories by solving, using the Alternating Direction Method of Multipliers (ADMM), a sequence of sub
Publikováno v:
ICRA
Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7530fada6e8580117a9131207155ad8e
Autor:
Alex Irpan, Sergey Levine, Vincent Vanhoucke, Konstantinos Bousmalis, Paul Wohlhart, Peter Pastor, Laura Downs, Julian Ibarz, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Kurt Konolige
Publikováno v:
ICRA
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c7f9eb38a322db476b954fd724bbbc1
Autor:
Alexander Herzog, Peter Pastor, Jeannette Bohg, Ludovic Righetti, Mrinal Kalakrishnan, Stefan Schaal, Tamim Asfour
Publikováno v:
Autonomous Robots
The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration