Search papers, labs, and topics across Lattice.
10
7
7
69
End-effector traces boost cross-embodiment transfer in robot manipulation, enhancing real-world task performance by 28% when leveraging simulation data.
Naive pretraining of Q-functions may hinder performance, while a simple ensemble approach can lead to over 1.26x improvement in fine-tuning effectiveness.
Frontier foundation models can directly control robots through visual interfaces, achieving remarkable success without any fine-tuning on robot-specific data.
SPIRAL achieves a remarkable 15% performance increase by combining sequential, parallel, and aggregative reasoning in language models.
Achieve near-perfect robotic manipulation with just 20 minutes of robot experience by smartly finetuning vision-language-action models with reinforcement learning.
Get the performance boost of expensive sampling-based RL policies for a fraction of the compute by learning to prune action candidates early in the diffusion denoising process.
Runners stick to their pace 60% better and enjoy the workout more when coached by a robot dog than when using an Apple Watch.
A robot can now play recognizable piano songs after just 30 minutes of real-world training, closing the sim-to-real gap for high-precision bimanual manipulation.
RADAR offers a scalable, interpretable framework for understanding robot policy generalization by directly linking test-time performance to the training data, revealing the specific types of generalization required.
Q-functions and implicit policy extraction are game-changers for batch online RL in robotics, unlocking significant performance gains over imitation-based approaches.
