103 citations · 151 across the 30 of their papers we have counts for
35 papers
KiRoSe: Using Kinematic Data for Efficient Segmentation of Robot Demonstrations
Shuangyu Xie, Kaiyuan Chen, Muhammad Zubair Irshad +3
Segmenting robot demonstration data into disjoint temporal and semantic subsegments is valuable for learning, cleaning, retrieval, and analysis. Segmentation is often performed man…
H2RBench: A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer
Chuyang Xiao, Haotian Zhan, Sriram Krishna +4
Learning robot manipulation policies from human video demonstrations constitutes a promising avenue for scalable robot learning. However, comparing different human-to-robot (H2R) t…
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Yunhai Han, Jianuo Qiu, Linhao Bai +14
Human manipulation videos are a convenient and intuitive source for robot learning. However, directly transferring human dexterity to robots remains challenging due to perception e…
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Junjie Ye, Rong Xue, Basile Van Hoorick +6
Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While vide…
Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
Andrii Zadaianchuk, Leonardo Barcellona, Lennard Schuenemann +7
Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulati…
AnyView: Synthesizing Any Novel View in Dynamic Scenes
Basile Van Hoorick, Dian Chen, Shun Iwase +7
Modern generative video models excel at producing convincing, high-quality outputs, but struggle to maintain multi-view and spatiotemporal consistency in highly dynamic real-world…
PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies
Arhan Jain, Mingtong Zhang, Kanav Arora +11
A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically cha…
EVE: A Generator-Verifier System for Generative Policies
Yusuf Ali, Gryphon Patlin, Karthik Kothuri +4
Visuomotor policies based on generative such as diffusion and flow-matching have shown strong performance for robotics applications but degrade under distribution shifts, demonstra…
AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis
Junjie Ye, Rong Xue, Basile Van Hoorick +4
The collection of large-scale and diverse robot demonstrations remains a major bottleneck for imitation learning, as real-world data acquisition is costly and simulators offer limi…
EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device
Gunjan Chhablani, Xiaomeng Ye, Muhammad Zubair Irshad +1
The field of Embodied AI predominantly relies on simulation for training and evaluation, often using either fully synthetic environments that lack photorealism or high-fidelity rea…
EscherNet++: Simultaneous Amodal Completion and Scalable View Synthesis through Masked Fine-Tuning and Enhanced Feed-Forward 3D Reconstruction
Xinan Zhang, Muhammad Zubair Irshad, Anthony Yezzi +2
We propose EscherNet++, a masked fine-tuned diffusion model that can synthesize novel views of objects in a zero-shot manner with amodal completion ability. Existing approaches uti…
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
Shengjie Lin, Jiading Fang, Muhammad Zubair Irshad +4
Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability…
GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity
Takuya Ikeda, Sergey Zakharov, Muhammad Zubair Irshad +8
We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video. Existing methods, while achieving impressive results, often strugg…
Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware
Justin Yu, Letian Fu, Huang Huang +5
Scaling robot learning requires vast and diverse datasets. Yet the prevailing data collection paradigm-human teleoperation-remains costly and constrained by manual effort and physi…
FastMap: Revisiting Structure from Motion through First-Order Optimization
Jiahao Li, Haochen Wang, Muhammad Zubair Irshad +4
We propose FastMap, a new global structure from motion method focused on speed and simplicity. Previous methods like COLMAP and GLOMAP are able to estimate high-precision camera po…
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping
Shun Iwase, Zubair Irshad, Katherine Liu +8
Robotic grasping is a cornerstone capability of embodied systems. Many methods directly output grasps from partial information without modeling the geometry of the scene, leading t…
Persistent Object Gaussian Splat (POGS) for Tracking Human and Robot Manipulation of Irregularly Shaped Objects
Justin Yu, Kush Hari, Karim El-Refai +6
Tracking and manipulating irregularly-shaped, previously unseen objects in dynamic environments is important for robotic applications in manufacturing, assembly, and logistics. Rec…
Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion
Vitor Guizilini, Muhammad Zubair Irshad, Dian Chen +2
Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-vi…
Neural Fields in Robotics: A Survey
Muhammad Zubair Irshad, Mauro Comi, Yen-Chen Lin +5
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dyna…
Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale Representations with a Mobile Robot
Justin Yu, Kush Hari, Kishore Srinivas +9
Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Langu…
RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning
Lawrence Yunliang Chen, Chenfeng Xu, Karthik Dharmarajan +6
Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emergi…
ICE-G: Image Conditional Editing of 3D Gaussian Splats
Vishnu Jaganathan, Hannah Hanyun Huang, Muhammad Zubair Irshad +3
Recently many techniques have emerged to create high quality 3D assets and scenes. When it comes to editing of these objects, however, existing approaches are either slow, compromi…
Learning 3D Robotics Perception using Inductive Priors
Muhammad Zubair Irshad
Recent advances in deep learning have led to a data-centric intelligence i.e. artificially intelligent models unlocking the potential to ingest a large amount of data and be really…