18 citations · 42 across the 10 of their papers we have counts for
6 papers · 1 filter
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations
Julen Urain, Ajay Mandlekar, Yilun Du +5
Learning from Demonstrations, the field that proposes to learn robot behavior models from data, is gaining popularity with the emergence of deep generative models. Although the pro…
Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models
Utkarsh A. Mishra, Shangjie Xue, Yongxin Chen +1
Long-horizon tasks, usually characterized by complex subtask dependencies, present a significant challenge in manipulation planning. Skill chaining is a practical approach to solvi…
Neural Field Dynamics Model for Granular Object Piles Manipulation
Shangjie Xue, Shuo Cheng, Pujith Kachana +1
We present a learning-based dynamics model for granular material manipulation. Inspired by the Eulerian approach commonly used in fluid dynamics, our method adopts a fully convolut…
Human-in-the-Loop Task and Motion Planning for Imitation Learning
Ajay Mandlekar, Caelan Garrett, Danfei Xu +1
Imitation learning from human demonstrations can teach robots complex manipulation skills, but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) s…
Zero-Shot Object Searching Using Large-scale Object Relationship Prior
Hongyi Chen, Ruinian Xu, Shuo Cheng +2
Home-assistant robots have been a long-standing research topic, and one of the biggest challenges is searching for required objects in housing environments. Previous object-goal na…
BITS: Bi-level Imitation for Traffic Simulation
Danfei Xu, Yuxiao Chen, Boris Ivanovic +1
Simulation is the key to scaling up validation and verification for robotic systems such as autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a…