79 citations · 218 across the 30 of their papers we have counts for
8 papers · 1 filter
GRaCE: Balancing Multiple Criteria to Achieve Stable, Collision-Free, and Functional Grasps
Tasbolat Taunyazov, Kelvin Lin, Harold Soh
This paper addresses the multi-faceted problem of robot grasping, where multiple criteria may conflict and differ in importance. We introduce a probabilistic framework, Grasp Ranki…
Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction
Kaiqi Chen, Jing Yu Lim, Kingsley Kuan +1
Perspective-taking is the ability to perceive or understand a situation or concept from another individual's point of view, and is crucial in daily human interactions. Enabling rob…
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
Alvin Heng, Harold Soh
The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content.…
Generative Modeling with Flow-Guided Density Ratio Learning
Alvin Heng, Abdul Fatir Ansari, Harold Soh
We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradie…
Large Language Models as Zero-Shot Human Models for Human-Robot Interaction
Bowen Zhang, Harold Soh
Human models play a crucial role in human-robot interaction (HRI), enabling robots to consider the impact of their actions on people and plan their behavior accordingly. However, c…
Translating Natural Language to Planning Goals with Large-Language Models
Yaqi Xie, Chen Yu, Tongyao Zhu +3
Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their appl…
Heterogeneous Beliefs and Multi-Population Learning in Network Games
Shuyue Hu, Harold Soh, Georgios Piliouras
The effect of population heterogeneity in multi-agent learning is practically relevant but remains far from being well-understood. Motivated by this, we introduce a model of multi-…
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Abdul Fatir Ansari, Alvin Heng, Andre Lim +1
Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task. One key issue is that the data generated by both natural…