5 citations · 13 across the 11 of their papers we have counts for
11 papers · 1 filter
Point Bridge: 3D Representations for Cross Domain Policy Learning
Siddhant Haldar, Lars Johannsmeier, Lerrel Pinto +4
Robot foundation models are beginning to deliver on the promise of generalist robotic agents, yet progress remains constrained by the scarcity of large-scale real-world manipulatio…
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
Joel Jang, Seonghyeon Ye, Zongyu Lin +25
We introduce DreamGen, a simple yet highly effective 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories - sy…
DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
Zhao Mandi, Yifan Hou, Dieter Fox +3
We study the problem of functional retargeting: learning dexterous manipulation policies to track object states from human hand-object demonstrations. We focus on long-horizon, bim…
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
NVIDIA, :, Johan Bjorck +40
General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist…
SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment
Caelan Garrett, Ajay Mandlekar, Bowen Wen +1
Imitation learning from human demonstrations is an effective paradigm for robot manipulation, but acquiring large datasets is costly and resource-intensive, especially for long-hor…
SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation
Zihan Zhou, Animesh Garg, Dieter Fox +2
Robot learning has proven to be a general and effective technique for programming manipulators. Imitation learning is able to teach robots solely from human demonstrations but is b…
Guiding Long-Horizon Task and Motion Planning with Vision Language Models
Zhutian Yang, Caelan Garrett, Dieter Fox +2
Vision-Language Models (VLM) can generate plausible high-level plans when prompted with a goal, the context, an image of the scene, and any planning constraints. However, there is…
Latent Action Pretraining from Videos
Seonghyeon Ye, Joel Jang, Byeongguk Jeon +13
We introduce Latent Action Pretraining for general Action models (LAPA), an unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot actio…
IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning
Ryan Hoque, Ajay Mandlekar, Caelan Garrett +2
Imitation learning is a promising paradigm for training robot control policies, but these policies can suffer from distribution shift, where the conditions at evaluation time diffe…
MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Ajay Mandlekar, Soroush Nasiriany, Bowen Wen +5
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely c…
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…