Research
Berkeley researchers tackle fragile long-horizon planning in learned world model
GRASP, a new gradient-based planner from UC Berkeley, addresses brittleness in long-horizon planning with learned world models by parallelizing optimization across time and reshaping gradients for cleaner action signals.
1 min read
SourceBerkeley AI Research
Researchers at UC Berkeley have released GRASP, a gradient-based planning method designed to make long-horizon planning with learned world models practical. The work, conducted with collaborators including Yann LeCun and Mike Rabbat, tackles a fundamental problem in robotics and embodied AI: existin...
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Method & sources
- Source type
- Primary publication (lab/vendor blog) — our analysis + implication
- Source link
- Berkeley AI Research
- Published
- UTC
- Byline
- By the gotcontext.ai team (editorial standards)
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