8 accepted papers
In this paper, we present a low-cost, easy-to-implement sim-to-real framework for biped locomotion that narrows the reality gap using only simulation data, without motion-capture or additional real-world measurements. First, a walking policy for the BRUCE robot is trained in Isaac Gym via reinforcem…
In this paper, we present a low-cost, easy-to-implement sim-to-real framework for biped locomotion that narrows the reality gap using only simulation data, without motion-capture or additional real-world measurements. First, a walking policy for the BRUCE robot is trained in Isaac Gym via reinforcem
Capturability characterizes a safe region of states for humanoid walking and is most commonly constructed by analyzing the one-dimensional divergent component of motion (DCM) of the center of mass. In this work, by exploiting the mathematical structure of the step-to-step (S2S) dynamics, we characte
Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specific control, and flexibility for de novo enzyme backbone generation. T…
Location-Based Social Networks (LBSNs) offer a rich dataset of user activity at Points-of-Interest (POIs), making next POI recommendation a key task. Traditional algorithms face challenges due to broad searching scopes, affecting recommendation accuracy. Users tend to visit nearby POIs and show temp…
In numerous user-centric services on mobile applications (apps), accurately mining user interests and generating effective user representations are paramount. Traditional approaches, which often involve training task-specific user representations, are becoming increasingly impractical due to their h…
User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning t…
Deep metric learning is crucial for finding an embedding function that can generalize to training and testing data, including unknown test classes. However, limited training samples restrict the model's generalization to downstream tasks. While adding new training samples is a promising solution, de…