71 citations · 93 across the 7 of their papers we have counts for
11 papers · 1 filter
AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
Jiafei Duan, Wilbert Pumacay, Nishanth Kumar +7
Robotic manipulation in open-world settings requires not only task execution but also the ability to detect and learn from failures. While recent advances in vision-language models…
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…
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong +7
Imitating human demonstrations is a promising approach to endow robots with various manipulation capabilities. While recent advances have been made in imitation learning and batch…
Learning Latent Actions to Control Assistive Robots
Dylan P. Losey, Hong Jun Jeon, Mengxi Li +5
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use…
Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control
Chen Wang, Rui Wang, Ajay Mandlekar +3
Imitation Learning (IL) is an effective framework to learn visuomotor skills from offline demonstration data. However, IL methods often fail to generalize to new scene configuratio…
Learning Multi-Arm Manipulation Through Collaborative Teleoperation
Albert Tung, Josiah Wong, Ajay Mandlekar +4
Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but ha…