Latest

• R²D²: Unlocking Robotic Assembly and Contact Rich Manipulation with NVIDIA Research
• MatchMaker: Automated Robotic Assembly Asset Generation for Policy Learning in Simulation
• SPOT: SE(3) Pose Trajectory Diffusion for Object-Centric Manipulation
• SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks
• FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
• AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries
• TacSL: A Library for Visuotactile Sensor Simulation and Learning
• MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
• IndustReal: Transferring Contact-Rich Assembly from Simulation to Reality
• DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
• DeXtreme: Transferring Agile In-hand Manipulation from Simulation to Reality
• Insights towards Sim2Real Contact-Rich Manipulation
• DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
• Factory: Fast Contact for Robotic Assembly
• Accelerated Policy Learning with Parallel Differentiable Simulation
• DefGraspSim: Physics-based simulation of grasp outcomes on 3D deformable objects
• Assistive Tele-op: Leveraging Transformers to Collect Robotic Task Demonstrations
• A differentiable simulator for robotic cutting
• DefGraspSim: Simulation-based grasping of 3D deformable objects
• DiSeCT: A differentiable simulation engine for autonomous robotic cutting
• DexYCB: A Benchmark for Capturing Hand Grasping of Objects
• Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections
• A Modeling Framework for Jamming Structures
• Interpreting and Predicting Tactile Signals for the SynTouch BioTac
• STReSSD: Sim-to-Real from Sound for Stochastic Dynamics
• Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework
• Inferring the Material Properties of Granular Media for Robotic Tasks