GCImOpt trains compact goal-conditioned neural policies by imitating efficiently generated optimal trajectories, achieving high success rates and near-optimal performance on cart-pole, quadcopter, and robot arm tasks while running thousands of times faster than optimization solvers.
Tianhao Zhang, Gregory Kahn, Sergey Levine, and Pieter Abbe el
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories
GCImOpt trains compact goal-conditioned neural policies by imitating efficiently generated optimal trajectories, achieving high success rates and near-optimal performance on cart-pole, quadcopter, and robot arm tasks while running thousands of times faster than optimization solvers.