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We propose optimal likelihood ratio-based selective classification methods based on the Neyman-Pearson lemma and evaluate them under vision and language covariate shifts tasks.
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.
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.
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.
