
RMPflow: A Geometric Framework for Generation of MultiTask Motion Policies
Generating robot motion for multiple tasks in dynamic environments is ch...
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Explaining Fast Improvement in Online Policy Optimization
Online policy optimization (OPO) views policy optimization for sequentia...
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Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Robotic tasks often require motions with complex geometric structures. W...
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In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods have become increas...
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Intra Orderpreserving Functions for Calibration of MultiClass Neural Networks
Predicting calibrated confidence scores for multiclass deep networks is...
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Information Theoretic Model Predictive QLearning
Modelfree Reinforcement Learning (RL) algorithms work well in sequentia...
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Continuous Online Learning and New Insights to Online Imitation Learning
Online learning is a powerful tool for analyzing iterative algorithms. H...
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Expressiveness and Learning of Hidden Quantum Markov Models
Extending classical probabilistic reasoning using the quantum mechanical...
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A Reduction from Reinforcement Learning to NoRegret Online Learning
We present a reduction from reinforcement learning (RL) to noregret onl...
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IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
Learning from offline task demonstrations is a problem of great interest...
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Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping
RMPflow is a recently proposed policyfusion framework based on differen...
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Trajectorywise Control Variates for Variance Reduction in Policy Gradient Methods
Policy gradient methods have demonstrated success in reinforcement learn...
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Online Motion Planning Over Multiple Homotopy Classes with Gaussian Process Inference
Efficient planning in dynamic and uncertain environments is a fundamenta...
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Differentiable Gaussian Process Motion Planning
Modern trajectory optimization based approaches to motion planning are f...
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Leveraging Experience in Lazy Search
Lazy graph search algorithms are efficient at solving motion planning pr...
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Provably Efficient Imitation Learning from Observation Alone
We study Imitation Learning (IL) from Observations alone (ILFO) in large...
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Composing Ensembles of Policies with Deep Reinforcement Learning
Composition of elementary skills into complex behaviors to solve challen...
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Learning to Find Common Objects Across Image Collections
We address the problem of finding a set of images containing a common, b...
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Stable, Concurrent Controller Composition for MultiObjective Robotic Tasks
Robotic systems often need to consider multiple tasks concurrently. This...
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Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold
Quantum graphical models (QGMs) extend the classical framework for reaso...
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Joint Inference of Kinematic and Force Trajectories with VisuoTactile Sensing
To perform complex tasks, robots must be able to interact with and manip...
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An Online Learning Approach to Model Predictive Control
Model predictive control (MPC) is a powerful technique for solving dynam...
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Online Learning with Continuous Variations: Dynamic Regret and Reductions
We study the dynamic regret of a new class of online learning problems, ...
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MultiObjective Policy Generation for MultiRobot Systems Using Riemannian Motion Policies
In the multirobot systems literature, control policies are typically ob...
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RMPflow: A Computational Graph for Automatic Motion Policy Generation
We develop a novel policy synthesis algorithm, RMPflow, based on geometr...
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Differentiable MPC for Endtoend Planning and Control
We present foundations for using Model Predictive Control (MPC) as a dif...
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Learning and Inference in Hilbert Space with Quantum Graphical Models
Quantum Graphical Models (QGMs) generalize classical graphical models by...
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Truncated Backpropagation for Bilevel Optimization
Bilevel optimization has been recently revisited for designing and analy...
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PredictorCorrector Policy Optimization
We present a predictorcorrector framework, called PicCoLO, that can tra...
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Robust Learning of Tactile Force Estimation through Robot Interaction
Current methods for estimating force from tactile sensor signals are eit...
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Orthogonally Decoupled Variational Gaussian Processes
Gaussian processes (GPs) provide a powerful nonparametric framework for...
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Learning to Align Images using Weak Geometric Supervision
Image alignment tasks require accurate pixel correspondences, which are ...
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Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
Learning from Demonstration (LfD) is a popular approach to endowing robo...
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STEAP: simultaneous trajectory estimation and planning
We present a unified probabilistic framework for simultaneous trajectory...
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Semantically Meaningful View Selection
An understanding of the nature of objects could help robots to solve bot...
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Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
For many image clustering problems, replacing raw image data with featur...
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ModelBased Imitation Learning with Accelerated Convergence
Sample efficiency is critical in solving realworld reinforcement learni...
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Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning
In this paper, we propose to combine imitation and reinforcement learnin...
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Dual Policy Iteration
Recently, a novel class of Approximate Policy Iteration (API) algorithms...
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Fast Policy Learning through Imitation and Reinforcement
Imitation learning (IL) consists of a set of tools that leverage expert ...
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Convergence of Value Aggregation for Imitation Learning
Value aggregation is a general framework for solving imitation learning ...
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Variational Inference for Gaussian Process Models with Linear Complexity
Largescale Gaussian process inference has long faced practical challeng...
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Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to hi...
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Learning Hidden Quantum Markov Models
Hidden Quantum Markov Models (HQMMs) can be thought of as quantum probab...
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Manifold Regularization for Kernelized LSTD
Policy evaluation or value function or Qfunction approximation is a key...
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PredictiveState Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rel...
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OneShot Learning for Semantic Segmentation
Lowshot learning methods for image classification support learning from...
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Predictive State Recurrent Neural Networks
We present a new model, Predictive State Recurrent Neural Networks (PSRN...
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4D Crop Monitoring: SpatioTemporal Reconstruction for Agriculture
Autonomous crop monitoring at high spatial and temporal resolution is a ...
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Learning from Conditional Distributions via Dual Embeddings
Many machine learning tasks, such as learning with invariance and policy...
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Byron Boots
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Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech