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Found 6 papers, 1 papers with code
$π_0$: A Vision-Language-Action Flow Model for General Robot Control
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence.
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
To the best of our knowledge, this is the first evidence that simulation transfer yields a provable gain in reinforcement learning in settings where direct sim2real transfer fails.
Real World Offline Reinforcement Learning with Realistic Data Source
Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience.
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation
Billions of people use chopsticks, a simple yet versatile tool, for fine manipulation of everyday objects.
Imitation Learning as $f$-Divergence Minimization
We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes.
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson et.
Ranked #4 on
Vision-Language Navigation
on Room2Room
