141 citations · 399 across the 34 of their papers we have counts for
6 papers · 1 filter
cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation
Balakumar Sundaralingam, Siva Kumar Sastry Hari, Adam Fishman +9
This paper explores the problem of collision-free motion generation for manipulators by formulating it as a global motion optimization problem. We develop a parallel optimization t…
NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors
Shuo Cheng, Caelan Garrett, Ajay Mandlekar +1
Solving complex manipulation tasks in household and factory settings remains challenging due to long-horizon reasoning, fine-grained interactions, and broad object and scene divers…
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…
Meta-Policy Learning over Plan Ensembles for Robust Articulated Object Manipulation
Constantinos Chamzas, Caelan Garrett, Balakumar Sundaralingam +2
Recent work has shown that complex manipulation skills, such as pushing or pouring, can be learned through state-of-the-art learning based techniques, such as Reinforcement Learnin…
DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
Xiaolin Fang, Caelan Reed Garrett, Clemens Eppner +3
Generative models such as diffusion models, excel at capturing high-dimensional distributions with diverse input modalities, e.g. robot trajectories, but are less effective at mult…
Imitating Task and Motion Planning with Visuomotor Transformers
Murtaza Dalal, Ajay Mandlekar, Caelan Garrett +3
Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. How…