41 citations · 58 across the 11 of their papers we have counts for
12 papers
Post-Training, Finetuning, and Steering:A Field Guide for Curating Training Data
Maria Attarian, Yifan Ruan, Claas Voelcker +5
While Vision-Language-Action models have enabled impressive cross-task transfer in zero-shot settings, \emph{adaptation} methods are often necessary to achieve consistent performan…
SeededGrasp: Language-Guided Grasping in Complex Scenes with Multiple Embodiments
Yang Xu, Gurpreet Singh Mukker, Raymond Wang +3
Practical robotic grasping in complex scenes requires both 3D spatial reasoning and alignment with task-specific requirements. Vision-language models (VLMs) offer a natural way to…
Update-Free On-Policy Steering via Verifiers
Maria Attarian, Ian Vyse, Jasper Gerigk +7
In recent years, Behavior Cloning (BC) has become one of the most prevalent methods for learning manipulation from human demonstrations. Despite their successes, BC policies are of…
BPP: Long-Context Robot Imitation Learning by Focusing on Key History Frames
Max Sobol Mark, Jacky Liang, Maria Attarian +4
Many robot tasks require attending to the history of past observations. For example, finding an item in a room requires remembering which places have already been searched. However…
GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping
Yunze Wei, Maria Attarian, Igor Gilitschenski
Despite recent progress on multi-finger dexterous grasping, current methods focus on single grippers and unseen objects, and even the ones that explore cross-embodiment, often fail…
Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers
Vidhi Jain, Maria Attarian, Nikhil J Joshi +10
Large-scale multi-task robotic manipulation systems often rely on text to specify the task. In this work, we explore whether a robot can learn by observing humans. To do so, the ro…
Learning to Learn Faster from Human Feedback with Language Model Predictive Control
Jacky Liang, Fei Xia, Wenhao Yu +47
Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot beha…
Geometry Matching for Multi-Embodiment Grasping
Maria Attarian, Muhammad Adil Asif, Jingzhou Liu +4
Many existing learning-based grasping approaches concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of…
See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction
Maria Attarian, Advaya Gupta, Ziyi Zhou +3
Cognitive planning is the structural decomposition of complex tasks into a sequence of future behaviors. In the computational setting, performing cognitive planning entails groundi…
Transporter Networks: Rearranging the Visual World for Robotic Manipulation
Andy Zeng, Pete Florence, Jonathan Tompson +9
Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass an object, part of an object, or end effector. In…
Transforming Neural Network Visual Representations to Predict Human Judgments of Similarity
Maria Attarian, Brett D. Roads, Michael C. Mozer
Deep-learning vision models have shown intriguing similarities and differences with respect to human vision. We investigate how to bring machine visual representations into better…
Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation
Pablo Samuel Castro, Maria Attarian
The use of language models for generating lyrics and poetry has received an increased interest in the last few years. They pose a unique challenge relative to standard natural lang…