Latest

• Learning for Kinodynamic Tree Expansion
• Stein Movement Primitives for Adaptive Multi-Modal Trajectory Generation
• AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries
• Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following
• Stein Random Feature Regression
• Stein Variational Ergodic Search
• Fast Fourier Bayesian Quadrature
• Learning to Simulate Tree-Branch Dynamics for Manipulation
• Path Signatures for Diversity in Probabilistic Trajectory Optimisation
• IndustReal: Transferring Contact-Rich Assembly from Simulation to Reality
• CuRobo: Parellelized Collision-Free Robot Motion Generation
• DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
• Global and Reactive Motion Generation with Geometric Fabric Command Sequences
• Bayesian Object Models for Robotic Interaction with Differentiable Probabilistic Programming
• Insights towards Sim2Real Contact-Rich Manipulation
• LTR*: Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph
• Optimal Transport Policy Fusion for Learning Diverse Skills
• DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
• Generalized Bayesian Quadrature with Spectral Kernels
• Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
• Adaptive Model Predictive Control by Learning Classifiers
• Diffeomorphic Transforms for Generalised Imitation Learning
• Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty
• Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation
• A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
• Accelerated Policy Learning with Parallel Differentiable Simulation
• Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
• Stein ICP for Uncertainty Estimation in Point Cloud Matching
• Adaptively Exploiting Local Structure with Generalised Multi-Trees Motion Planning
• Parallelised Diffeomorphic Sampling-based Motion Planning
• STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation
• PlannerFlows: Learning Motion Samplers with Normalising Flows
• Probabilistic Trajectory Prediction with Structural Constraints
• Trajectory Generation in New Environments from Past Experiences
• A differentiable simulator for robotic cutting
• BORE: Bayesian Optimization by Density-Ratio Estimation
• DiSeCT: A differentiable simulation engine for autonomous robotic cutting
• Dual Online Stein Variational Inference for Control and Dynamics
• No-Regret Approximate Inference via Bayesian Optimisation
• Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
• Fast Uncertainty Quantification for Deep Object Pose Estimation
• Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
• A User's Guide to Calibrating Robotics Simulators
• Stein Variational Model Predictive Control
• STReSSD: Sim-to-Real from Sound for Stochastic Dynamics
• Heteroscedastic Bayesian Optimization for Stochastic Model Predictive Control
• Online BayesSim for Combined Simulator Parameter Inference and Policy Improvement
• Active learning of conditional mean embeddings via Bayesian optimisation
• Online Domain Adaptation for Occupancy Mapping
• DISCO: Double Likelihood-Free Inference Stochastic Control
• Estimating Motion Uncertainty with Bayesian ICP
• Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
• Guided Uncertainty-Aware Policy Optimization: Combining Model-Free and Model-Based Strategies for Sample-Efficient Learning
• Implicit Reinforcement without Interaction at Scale: Leveraging Large-Scale Robot Manipulation Datasets for Control
• Inferring the Material Properties of Granular Media for Robotic Tasks
• Combining Model-Free and Model-Based Strategies for Sample-Efficient Reinforcement Learning
• Distributional Bayesian optimisation for variational inference on black-box simulators
• Optimal Transport for Distribution Adaptation in Bayesian Hilbert Maps
• Learning to Plan Hierarchically from Curriculum
• Spatiotemporal Learning of Directional Uncertainty in Urban Environments
• Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction
• Periodic Kernel Approximation by Index Set Fourier Series Features
• Bayesian Deconditional Kernel Mean Embeddings
• BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators
• Occupancy map building through Bayesian exploration