524 citations · 635 across the 13 of their papers we have counts for
14 papers
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators
Alexander Herzog, Kanishka Rao, Karol Hausman +37
We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Rea…
Jump-Start Reinforcement Learning
Ikechukwu Uchendu, Ted Xiao, Yao Lu +9
Reinforcement learning (RL) provides a theoretical framework for continuously improving an agent's behavior via trial and error. However, efficiently learning policies from scratch…
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Michael Ahn, Anthony Brohan, Noah Brown +42
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extend…
AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale
Yao Lu, Karol Hausman, Yevgen Chebotar +8
Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experi…
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…
Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control
Mengyuan Yan, Iuri Frosio, Stephen Tyree +1
In the context of deep learning for robotics, we show effective method of training a real robot to grasp a tiny sphere (1.37cm of diameter), with an original combination of system…
Convolutional Imputation of Matrix Networks
Qingyun Sun, Mengyuan Yan David Donoho, Stephen Boyd
A matrix network is a family of matrices, with relatedness modeled by a weighted graph. We consider the task of completing a partially observed matrix network. We assume a novel sa…
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Charles R. Qi, Hao Su, Matthias Niessner +3
3D shape models are becoming widely available and easier to capture, making available 3D information crucial for progress in object classification. Current state-of-the-art methods…
3D Reconstruction from Full-view Fisheye Camera
Chuiwen Ma, Liang Shi, Hanlu Huang +1
In this report, we proposed a 3D reconstruction method for the full-view fisheye camera. The camera we used is Ricoh Theta, which captures spherical images and has a wide field of…