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06/16/2023
CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
We present CAJun, a novel hierarchical learning and control framework th...
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05/04/2023
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula
Autocurricular training is an important sub-area of multi-agent reinforc...
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04/17/2023
Continuous Versatile Jumping Using Learned Action Residuals
Jumping is essential for legged robots to traverse through difficult ter...
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04/03/2023
Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models
Simulating vision-based tactile sensors enables learning models for cont...
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03/30/2023
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
We study a new paradigm for sequential decision making, called offline P...
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03/28/2023
TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation
Effective use of camera-based vision systems is essential for robust per...
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02/21/2023
Adversarial Model for Offline Reinforcement Learning
We propose a novel model-based offline Reinforcement Learning (RL) frame...
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12/05/2022
Learning to Optimize in Model Predictive Control
Sampling-based Model Predictive Control (MPC) is a flexible control fram...
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12/05/2022
Learning Sampling Distributions for Model Predictive Control
Sampling-based methods have become a cornerstone of contemporary approac...
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10/21/2022
Motion Policy Networks
Collision-free motion generation in unknown environments is a core build...
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10/17/2022
Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing
We present Neural Contact Fields, a method that brings together neural f...
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06/27/2022
Learning Semantics-Aware Locomotion Skills from Human Demonstration
The semantics of the environment, such as the terrain type and property,...
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04/11/2022
Learning Implicit Priors for Motion Optimization
In this paper, we focus on the problem of integrating Energy-based Model...
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02/14/2022
Motivating Physical Activity via Competitive Human-Robot Interaction
This project aims to motivate research in competitive human-robot intera...
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11/15/2021
Nonprehensile Riemannian Motion Predictive Control
Nonprehensile manipulation involves long horizon underactuated object in...
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11/04/2021
Stein Variational Probabilistic Roadmaps
Efficient and reliable generation of global path plans are necessary for...
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09/21/2021
Geometric Fabrics: Generalizing Classical Mechanics to Capture the Physics of Behavior
Classical mechanical systems are central to controller design in energy ...
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07/11/2021
Entropy Regularized Motion Planning via Stein Variational Inference
Many Imitation and Reinforcement Learning approaches rely on the availab...
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06/16/2021
Safe Reinforcement Learning Using Advantage-Based Intervention
Many sequential decision problems involve finding a policy that maximize...
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05/07/2021
CoDE: Collocation for Demonstration Encoding
Roboticists frequently turn to Imitation learning (IL) for data efficien...
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04/28/2021
Fast Joint Space Model-Predictive Control for Reactive Manipulation
Sampling-based model predictive control (MPC) is a promising tool for fe...
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04/09/2021
Fast and Efficient Locomotion via Learned Gait Transitions
We focus on the problem of developing efficient controllers for quadrupe...
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04/07/2021
The Value of Planning for Infinite-Horizon Model Predictive Control
Model Predictive Control (MPC) is a classic tool for optimal control of ...
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03/25/2021
Few-shot Weakly-Supervised Object Detection via Directional Statistics
Detecting novel objects from few examples has become an emerging topic i...
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03/23/2021
Dual Online Stein Variational Inference for Control and Dynamics
Model predictive control (MPC) schemes have a proven track record for de...
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03/10/2021
RMP2: A Structured Composable Policy Class for Robot Learning
We consider the problem of learning motion policies for acceleration-bas...
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01/07/2021
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Recently, a common starting point for solving complex unsupervised image...
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12/24/2020
Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees
Robotic tasks often require generation of motions that satisfy multiple ...
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12/10/2020
Blending MPC Value Function Approximation for Efficient Reinforcement Learning
Model-Predictive Control (MPC) is a powerful tool for controlling comple...
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11/15/2020
Stein Variational Model Predictive Control
Decision making under uncertainty is critical to real-world, autonomous ...
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11/13/2020
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation
Billions of people use chopsticks, a simple yet versatile tool, for fine...
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10/28/2020
Geometric Fabrics for the Acceleration-based Design of Robotic Motion
This paper describes the pragmatic design and construction of geometric ...
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10/20/2020
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Modeling joint probability distributions over sequences has been studied...
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09/21/2020
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
We present a hierarchical framework that combines model-based control an...
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07/25/2020
RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies
Generating robot motion for multiple tasks in dynamic environments is ch...
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07/06/2020
Explaining Fast Improvement in Online Policy Optimization
Online policy optimization (OPO) views policy optimization for sequentia...
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05/27/2020
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Robotic tasks often require motions with complex geometric structures. W...
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03/18/2020
In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods have become increas...
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03/15/2020
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Predicting calibrated confidence scores for multi-class deep networks is...
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12/31/2019
Information Theoretic Model Predictive Q-Learning
Model-free Reinforcement Learning (RL) algorithms work well in sequentia...
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12/03/2019
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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12/02/2019
Expressiveness and Learning of Hidden Quantum Markov Models
Extending classical probabilistic reasoning using the quantum mechanical...
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11/14/2019
A Reduction from Reinforcement Learning to No-Regret Online Learning
We present a reduction from reinforcement learning (RL) to no-regret onl...
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11/13/2019
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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10/07/2019
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping
RMPflow is a recently proposed policy-fusion framework based on differen...
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08/08/2019
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods
Policy gradient methods have demonstrated success in reinforcement learn...
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08/01/2019
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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07/22/2019
Differentiable Gaussian Process Motion Planning
Modern trajectory optimization based approaches to motion planning are f...
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07/16/2019
Leveraging Experience in Lazy Search
Lazy graph search algorithms are efficient at solving motion planning pr...
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05/27/2019