KOAP: Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers
We introduce KOAP for imitation learning with limited actions.
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SocRATES: Towards Automated Scenario-based Testing of Social Navigation Algorithms
We design an LLM-driven social-scenario simulation pipeline (SocRATES) to enable more holistic evaluation of social navigation algorithms. SocRATES generates context- and location-appropriate scenarios from simple text and image-based inputs, thus reducing the labor-intensive task of scenario proposal and synthesis that is typically required for scenario-based testing.
Out-of-Distribution Detection with a Single Unconditional Diffusion Model
We show that a single unconditional diffusion model performs competitively in out-of-distribution detection tasks by measuring the rate-of-change and curvature of diffusion paths connecting data samples to the standard normal distribution.
Stochastic Bandits for Egalitarian Assignment
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.
LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based Planning
We develop a safe planning method for trajectory generation by sampling from diffusion model under different LTLf constraints.
Generative Modeling with Flow-Guided Density Ratio Learning
We extend gradient flow methods to a variety of high-quality image synthesis tasks using a novel density ratio learning method.
Harold delivers ICRA'24 Keynote
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...
