5 papers
3D Point World Models: Point Completion Enables More Accurate Dynamics Learning
Skand Peri, Hung Nguyen, Chanho Kim +2
Learning predictive models of the world enables robotic control through planning, potentially allowing robots to improvise solutions on new tasks. However, large video-based dynami…
Learning a Particle Dynamics Model with Real-world Videos
Chanho Kim, Suhas V. Sumukh, Li Fuxin
Data-driven learning approaches for physics simulation, sometimes referred to as world models, have emerged as promising alternatives to traditional physics simulators due to their…
Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement
Minku Kim, Kuan-Chia Chen, Aayam Shrestha +3
We investigate a skill-based framework for humanoid box rearrangement that enables long-horizon execution by sequencing reusable skills at the task level. In our architecture, all…
Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods
Mingqi Jiang, Saeed Khorram, Li Fuxin
In order to gain insights about the decision-making of different visual recognition backbones, we propose two methodologies, sub-explanation counting and cross-testing, that system…
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions
Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi +2
Despite extensive research on training generative adversarial networks (GANs) with limited training data, learning to generate images from long-tailed training distributions remain…