3.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Nvidia (United Kingdom)GB136 papers
- Lawrence Berkeley National LaboratoryUS32 papers
- Stanford UniversityUS30 papers
- University of California, BerkeleyUS29 papers
- California Institute of TechnologyUS26 papers
- Massachusetts Institute of TechnologyUS25 papers
- Carnegie Mellon UniversityUS24 papers
- University of Illinois Urbana-ChampaignUS24 papers
- University of TorontoCA24 papers
- University of WashingtonUS23 papers
- Harvard UniversityUS19 papers
- Georgia Institute of TechnologyUS17 papers
33 papers · 1 filter
MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation
Kourosh Darvish, Arjun Sohal, Abhijoy Mandal +20
Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and thi…
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Zhengyi Luo, Ye Yuan, Tingwu Wang +25
Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid contro…
TacSL: A Library for Visuotactile Sensor Simulation and Learning
Iretiayo Akinola, Jie Xu, Jan Carius +2
For both humans and robots, the sense of touch, known as tactile sensing, is critical for performing contact-rich manipulation tasks. Three key challenges in robotic tactile sensin…
Benchmarking Population-Based Reinforcement Learning across Robotic Tasks with GPU-Accelerated Simulation
Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone +5
In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks. However, this comes at the cost of an enormous amount of…
Pick and Place Planning is Better than Pick Planning then Place Planning
Mohanraj Devendran Shanthi, Tucker Hermans
Robotic pick and place stands at the heart of autonomous manipulation. When conducted in cluttered or complex environments robots must jointly reason about the selected grasp and d…
Constrained Generative Sampling of 6-DoF Grasps
Jens Lundell, Francesco Verdoja, Tran Nguyen Le +3
Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable…
mBEST: Realtime Deformable Linear Object Detection Through Minimal Bending Energy Skeleton Pixel Traversals
Andrew Choi, Dezhong Tong, Brian Park +3
Robotic manipulation of deformable materials is a challenging task that often requires realtime visual feedback. This is especially true for deformable linear objects (DLOs) or "ro…
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
Mayank Mittal, Calvin Yu, Qinxi Yu +12
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments w…
Learning Neural Force Manifolds for Sim2Real Robotic Symmetrical Paper Folding
Andrew Choi, Dezhong Tong, Demetri Terzopoulos +2
Robotic manipulation of slender objects is challenging, especially when the induced deformations are large and nonlinear. Traditionally, learning-based control approaches, such as…
DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner +5
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery…
Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
Tim Salzmann, Elia Kaufmann, Jon Arrizabalaga +3
Model Predictive Control (MPC) has become a popular framework in embedded control for high-performance autonomous systems. However, to achieve good control performance using MPC, a…
PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks
Jiankai Sun, De-An Huang, Bo Lu +3
In this work, we study the problem of how to leverage instructional videos to facilitate the understanding of human decision-making processes, focusing on training a model with the…
Learning Composable Behavior Embeddings for Long-horizon Visual Navigation
Xiangyun Meng, Yu Xiang, Dieter Fox
Learning high-level navigation behaviors has important implications: it enables robots to build compact visual memory for repeating demonstrations and to build sparse topological m…
Reactive Human-to-Robot Handovers of Arbitrary Objects
Wei Yang, Chris Paxton, Arsalan Mousavian +3
Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing o…
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Yu Xiang, Christopher Xie, Arsalan Mousavian +1
Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method…
Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control
Ian Abraham, Ankur Handa, Nathan Ratliff +3
This work addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy form…
Human Grasp Classification for Reactive Human-to-Robot Handovers
Wei Yang, Chris Paxton, Maya Cakmak +1
Transfer of objects between humans and robots is a critical capability for collaborative robots. Although there has been a recent surge of interest in human-robot handovers, most p…
Transferable Task Execution from Pixels through Deep Planning Domain Learning
Kei Kase, Chris Paxton, Hammad Mazhar +2
While robots can learn models to solve many manipulation tasks from raw visual input, they cannot usually use these models to solve new problems. On the other hand, symbolic planni…
In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation
Jacky Liang, Ankur Handa, Karl Van Wyk +3
Tracking the pose of an object while it is being held and manipulated by a robot hand is difficult for vision-based methods due to significant occlusions. Prior works have explored…
Benchmarking In-Hand Manipulation
Silvia Cruciani, Balakumar Sundaralingam, Kaiyu Hang +3
The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the system's ability to change the pose…
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity
Ajay Mandlekar, Jonathan Booher, Max Spero +6
Large, richly annotated datasets have accelerated progress in fields such as computer vision and natural language processing, but replicating these successes in robotics has been c…
Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection
Lirui Wang, Yu Xiang, Dieter Fox
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem. A manipulation planner needs to generate a trajectory of the manipula…
Map as The Hidden Sensor: Fast Odometry-Based Global Localization
Cheng Peng, David Weikersdorfer
Accurate and robust global localization is essential to robotics applications. We propose a novel global localization method that employs the map traversability as a hidden observa…
Scaling Local Control to Large-Scale Topological Navigation
Xiangyun Meng, Nathan Ratliff, Yu Xiang +1
Visual topological navigation has been revitalized recently thanks to the advancement of deep learning that substantially improves robot perception. However, the scalability and re…