524 citations · 635 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…