29 citations · 72 across the 6 of their papers we have counts for
9 papers
Learning Topological Motion Primitives for Knot Planning
Mengyuan Yan, Gen Li, Yilin Zhu +1
In this paper, we approach the challenging problem of motion planning for knot tying. We propose a hierarchical approach in which the top layer produces a topological plan and the…
GRAC: Self-Guided and Self-Regularized Actor-Critic
Lin Shao, Yifan You, Mengyuan Yan +2
Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a range of challenging decision making and control tasks. One dominant component of recent deep…
How to Close Sim-Real Gap? Transfer with Segmentation!
Mengyuan Yan, Qingyun Sun, Iuri Frosio +2
One fundamental difficulty in robotic learning is the sim-real gap problem. In this work, we propose to use segmentation as the interface between perception and control, as a domai…
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects
Mengyuan Yan, Yilin Zhu, Ning Jin +1
We demonstrate model-based, visual robot manipulation of linear deformable objects. Our approach is based on a state-space representation of the physical system that the robot aims…
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
Xingyu Liu, Mengyuan Yan, Jeannette Bohg
Understanding dynamic 3D environment is crucial for robotic agents and many other applications. We propose a novel neural network architecture called for learning repre…
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Mengyuan Yan, Adrian Li, Mrinal Kalakrishnan +1
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…