6 papers
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…
Grounding Vision and Language to 3D Masks for Long-Horizon Box Rearrangement
Ashish Malik, Caleb Lowe, Aayam Shrestha +3
We study long-horizon planning in 3D environments from under-specified natural-language goals using only visual observations, focusing on multi-step 3D box rearrangement tasks. Exi…
Long-Term 3D Point Tracking By Cost Volume Fusion
Hung Nguyen, Chanho Kim, Rigved Naukarkar +1
Long-term point tracking is essential to understand non-rigid motion in the physical world better. Deep learning approaches have recently been incorporated into long-term point tra…
Point Cloud Models Improve Visual Robustness in Robotic Learners
Skand Peri, Iain Lee, Chanho Kim +3
Visual control policies can encounter significant performance degradation when visual conditions like lighting or camera position differ from those seen during training -- often ex…
Object Dynamics Modeling with Hierarchical Point Cloud-based Representations
Chanho Kim, Li Fuxin
Modeling object dynamics with a neural network is an important problem with numerous applications. Most recent work has been based on graph neural networks. However, physics happen…
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Yixuan Huang, Jialin Yuan, Chanho Kim +4
Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-orient…