Laruso blog features productivity, tips, inspiration and strategies for massive profits. Find out how to set up a successful blog or how to make yours even better!
We address a problem where an agent assigns users to arms in a stochastic multi-armed bandit setting to maximize the minimum expected cumulative reward for all users. It presents a UCB-based policy with upper bounds on cumulative regret and an impossibility result for policy-independent approaches.
We develop a safe planning method for trajectory generation by sampling from diffusion model under different LTLf constraints.
We extend gradient flow methods to a variety of high-quality image synthesis tasks using a novel density ratio learning method.
Harold gave a well-received Keynote at ICRA’24 on our group’s work on generative modeling and robotics! He spoke about two of our recent works on new kinds o...
We introduce PhysiCLeaR, an annotated dataset of everyday objects and tactile readings collected from a Gelsight Mini sensor, as well as Octopi, a system that leverages both tactile representation learning and large vision-language models to perform physical reasoning and inference, given tactile videos of multiple objects.
All four of our submissions (3 papers, 1 demo) were accepted to R:SS 2024! A fantastic accomplishment by our CLeAR members Kaiqi, Samson, Tasbolat, Linh, Kel...
We develop interpolant policies that leverage informative source distributions for imitation learning.
