31 citations · 82 across the 14 of their papers we have counts for
14 papers
A Critical Evaluation of AI Feedback for Aligning Large Language Models
Archit Sharma, Sedrick Keh, Eric Mitchell +3
Reinforcement learning with AI feedback (RLAIF) is a popular paradigm for improving the instruction-following abilities of powerful pre-trained language models. RLAIF first perform…
NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu +5
Recent implicit neural representations have shown great results for novel view synthesis. However, existing methods require expensive per-scene optimization from many views hence l…
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov +5
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a…
Bagging by Learning to Singulate Layers Using Interactive Perception
Lawrence Yunliang Chen, Baiyu Shi, Roy Lin +6
Many fabric handling and 2D deformable material tasks in homes and industry require singulating layers of material such as opening a bag or arranging garments for sewing. In contra…
HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation
Vainavi Viswanath, Kaushik Shivakumar, Jainil Ajmera +6
Tracing - estimating the spatial state of - long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factorie…
Language-Driven Representation Learning for Robotics
Siddharth Karamcheti, Suraj Nair, Annie S. Chen +4
Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. Leveraging methods…
AutoBag: Learning to Open Plastic Bags and Insert Objects
Lawrence Yunliang Chen, Baiyu Shi, Daniel Seita +4
Thin plastic bags are ubiquitous in retail stores, healthcare, food handling, recycling, homes, and school lunchrooms. They are challenging both for perception (due to specularitie…
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception
Kaushik Shivakumar, Vainavi Viswanath, Anrui Gu +6
Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling. This paper extends prior work on autonomously untangling long cables by introducing…
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
Muhammad Zubair Irshad, Sergey Zakharov, Rares Ambrus +3
Our method studies the complex task of object-centric 3D understanding from a single RGB-D observation. As it is an ill-posed problem, existing methods suffer from low performance…
Efficiently Learning Single-Arm Fling Motions to Smooth Garments
Lawrence Yunliang Chen, Huang Huang, Ellen Novoseller +6
Recent work has shown that 2-arm "fling" motions can be effective for garment smoothing. We consider single-arm fling motions. Unlike 2-arm fling motions, which require little robo…
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
Muhammad Zubair Irshad, Thomas Kollar, Michael Laskey +2
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation. In contrast to instance-level…
SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo
Thomas Kollar, Michael Laskey, Kevin Stone +2
Robot manipulation of unknown objects in unstructured environments is a challenging problem due to the variety of shapes, materials, arrangements and lighting conditions. Even with…
A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
Max Bajracharya, James Borders, Dan Helmick +10
We describe a mobile manipulation hardware and software system capable of autonomously performing complex human-level tasks in real homes, after being taught the task with a single…
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Thomas Kollar, Stefanie Tellex, Matthew Walter +8
Many task domains require robots to interpret and act upon natural language commands which are given by people and which refer to the robot's physical surroundings. Such interpreta…