Reinforcement learning on variable impedance controller for high-precision robotic assembly
J Luo, E Solowjow, C Wen, JA Ojea… - … on robotics and …, 2019 - ieeexplore.ieee.org
Precise robotic manipulation skills are desirable in many industrial settings, reinforcement
learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, …
learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, …
Learning robotic assembly from cad
In this work, motivated by recent manufacturing trends, we investigate autonomous robotic
assembly. Industrial assembly tasks require contact-rich manipulation skills, which are …
assembly. Industrial assembly tasks require contact-rich manipulation skills, which are …
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics …
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics …
Deep reinforcement learning for robotic assembly of mixed deformable and rigid objects
J Luo, E Solowjow, C Wen, JA Ojea… - 2018 IEEE/RSJ …, 2018 - ieeexplore.ieee.org
Reinforcement learning for assembly tasks can yield powerful robot control algorithms for
applications that are challenging or even impossible for “conventional” feedback control …
applications that are challenging or even impossible for “conventional” feedback control …
Meta-reinforcement learning for robotic industrial insertion tasks
Robotic insertion tasks are characterized by contact and friction mechanics, making them
challenging for conventional feedback control methods due to unmodeled physical effects. …
challenging for conventional feedback control methods due to unmodeled physical effects. …
LFZip: Lossy compression of multivariate floating-point time series data via improved prediction
…, K Tatwawadi, C Wen, L Wang, JA Ojea… - 2020 Data …, 2020 - ieeexplore.ieee.org
Time series data compression is emerging as an important problem with the growth in IoT
devices and sensors. Due to the presence of noise in these datasets, lossy compression can …
devices and sensors. Due to the presence of noise in these datasets, lossy compression can …
Domain randomization for active pose estimation
X Ren, J Luo, E Solowjow, JA Ojea… - … on Robotics and …, 2019 - ieeexplore.ieee.org
Accurate state estimation is a fundamental component of robotic control. In robotic
manipulation tasks, as is our focus in this work, state estimation is essential for identifying the …
manipulation tasks, as is our focus in this work, state estimation is essential for identifying the …
Residual reinforcement learning for robot control
Conventional feedback control methods can solve various types of robot control problems
very efficiently by capturing the structure with explicit models, such as rigid body equations of …
very efficiently by capturing the structure with explicit models, such as rigid body equations of …
Combining power and communication network simulation for cost-effective smart grid analysis
K Mets, JA Ojea, C Develder - IEEE Communications Surveys & …, 2014 - ieeexplore.ieee.org
Today's electricity grid is transitioning to a so-called smart grid. The associated challenges
and funding initiatives have spurred great efforts from the research community to propose …
and funding initiatives have spurred great efforts from the research community to propose …
Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards
Connector insertion and many other tasks commonly found in modern manufacturing
settings involve complex contact dynamics and friction. Since it is difficult to capture related …
settings involve complex contact dynamics and friction. Since it is difficult to capture related …