GROOT
Collection
GROOT is a research series investigating how self-supervised and weakly supervised learning can be used to train agents that follow instructions.
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3 items
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Updated
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1
Shaofei Cai
phython96
AI & ML interests
Embodied Decision Making, Computer Vision, Game AI, Robotics
Recent Activity
updated
a collection
3 days ago
GROOT
authored
a paper
3 days ago
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents
upvoted
a
collection
5 days ago
GROOT
Organizations
phython96's activity
updated
a
collection
3 days ago
authored
a
paper
3 days ago
upvoted
a
collection
5 days ago
upvoted
a
paper
8 days ago
commented
7
papers
8 days ago
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Paper
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2503.02505
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Published
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6
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10
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Paper
•
2503.02505
•
Published
•
6
•
10
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Paper
•
2503.02505
•
Published
•
6
•
10
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Paper
•
2503.02505
•
Published
•
6
•
10
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Paper
•
2503.02505
•
Published
•
6
•
10
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Paper
•
2503.02505
•
Published
•
6
•
10
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Paper
•
2503.02505
•
Published
•
6
•
10
updated
2
collections
8 days ago