b'Byron Boots'

research

∙ 06/16/2023

CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller

We present CAJun, a novel hierarchical learning and control framework th...

0 Yuxiang Yang, et al. ∙

research

∙ 05/04/2023

Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula

Autocurricular training is an important sub-area of multi-agent reinforc...

0 Boling Yang, et al. ∙

research

∙ 04/17/2023

Continuous Versatile Jumping Using Learned Action Residuals

Jumping is essential for legged robots to traverse through difficult ter...

5 Yuxiang Yang, et al. ∙

research

∙ 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...

0 Carolina Higuera, et al. ∙

research

∙ 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...

0 Anqi Li, et al. ∙

research

∙ 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...

0 Xiangyun Meng, et al. ∙

research

∙ 02/21/2023

Adversarial Model for Offline Reinforcement Learning

We propose a novel model-based offline Reinforcement Learning (RL) frame...

0 Mohak Bhardwaj, et al. ∙

research

∙ 12/05/2022

Learning to Optimize in Model Predictive Control

Sampling-based Model Predictive Control (MPC) is a flexible control fram...

0 Jacob Sacks, et al. ∙

research

∙ 12/05/2022

Learning Sampling Distributions for Model Predictive Control

Sampling-based methods have become a cornerstone of contemporary approac...

0 Jacob Sacks, et al. ∙

research

∙ 10/21/2022

Motion Policy Networks

Collision-free motion generation in unknown environments is a core build...

0 Adam Fishman, et al. ∙

research

∙ 10/17/2022

Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing

We present Neural Contact Fields, a method that brings together neural f...

5 Carolina Higuera, et al. ∙

research

∙ 06/27/2022

Learning Semantics-Aware Locomotion Skills from Human Demonstration

The semantics of the environment, such as the terrain type and property,...

10 Yuxiang Yang, et al. ∙

research

∙ 04/11/2022

Learning Implicit Priors for Motion Optimization

In this paper, we focus on the problem of integrating Energy-based Model...

4 Alexander Lambert, et al. ∙

research

∙ 02/14/2022

Motivating Physical Activity via Competitive Human-Robot Interaction

This project aims to motivate research in competitive human-robot intera...

0 Boling Yang, et al. ∙

research

∙ 11/15/2021

Nonprehensile Riemannian Motion Predictive Control

Nonprehensile manipulation involves long horizon underactuated object in...

0 Hamid Izadinia, et al. ∙

research

∙ 11/04/2021

Stein Variational Probabilistic Roadmaps

Efficient and reliable generation of global path plans are necessary for...

0 Alexander Lambert, et al. ∙

research

∙ 09/21/2021

Geometric Fabrics: Generalizing Classical Mechanics to Capture the Physics of Behavior

Classical mechanical systems are central to controller design in energy ...

0 Karl Van Wyk, et al. ∙

research

∙ 07/11/2021

Entropy Regularized Motion Planning via Stein Variational Inference

Many Imitation and Reinforcement Learning approaches rely on the availab...

0 Alexander Lambert, et al. ∙

research

∙ 06/16/2021

Safe Reinforcement Learning Using Advantage-Based Intervention

Many sequential decision problems involve finding a policy that maximize...

0 Nolan Wagener, et al. ∙

research

∙ 05/07/2021

CoDE: Collocation for Demonstration Encoding

Roboticists frequently turn to Imitation learning (IL) for data efficien...

2 Mandy Xie, et al. ∙

research

∙ 04/28/2021

Fast Joint Space Model-Predictive Control for Reactive Manipulation

Sampling-based model predictive control (MPC) is a promising tool for fe...

0 Mohak Bhardwaj, et al. ∙

research

∙ 04/09/2021

Fast and Efficient Locomotion via Learned Gait Transitions

We focus on the problem of developing efficient controllers for quadrupe...

3 Yuxiang Yang, et al. ∙

research

∙ 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 ...

0 Nathan Hatch, et al. ∙

research

∙ 03/25/2021

Few-shot Weakly-Supervised Object Detection via Directional Statistics

Detecting novel objects from few examples has become an emerging topic i...

4 Amirreza Shaban, et al. ∙

research

∙ 03/23/2021

Dual Online Stein Variational Inference for Control and Dynamics

Model predictive control (MPC) schemes have a proven track record for de...

0 Lucas Barcelos, et al. ∙

research

∙ 03/10/2021

RMP2: A Structured Composable Policy Class for Robot Learning

We consider the problem of learning motion policies for acceleration-bas...

0 Anqi Li, et al. ∙

research

∙ 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...

14 Joris Guérin, et al. ∙

research

∙ 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 ...

6 M. Asif Rana, et al. ∙

research

∙ 12/10/2020

Blending MPC Value Function Approximation for Efficient Reinforcement Learning

Model-Predictive Control (MPC) is a powerful tool for controlling comple...

0 Mohak Bhardwaj, et al. ∙

research

∙ 11/15/2020

Stein Variational Model Predictive Control

Decision making under uncertainty is critical to real-world, autonomous ...

7 Alexander Lambert, et al. ∙

research

∙ 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...

0 Liyiming Ke, et al. ∙

research

∙ 10/28/2020

Geometric Fabrics for the Acceleration-based Design of Robotic Motion

This paper describes the pragmatic design and construction of geometric ...

0 Mandy Xie, et al. ∙

research

∙ 10/20/2020

Quantum Tensor Networks, Stochastic Processes, and Weighted Automata

Modeling joint probability distributions over sequences has been studied...

0 Siddarth Srinivasan, et al. ∙

research

∙ 09/21/2020

Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion

We present a hierarchical framework that combines model-based control an...

0 Xingye Da, et al. ∙

research

∙ 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...

0 Ching-An Cheng, et al. ∙

research

∙ 07/06/2020

Explaining Fast Improvement in Online Policy Optimization

Online policy optimization (OPO) views policy optimization for sequentia...

0 Xinyan Yan, et al. ∙

research

∙ 05/27/2020

Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems

Robotic tasks often require motions with complex geometric structures. W...

0 Muhammad Asif Rana, et al. ∙

research

∙ 03/18/2020

In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization

Weakly Supervised Object Localization (WSOL) methods have become increas...

8 Amir Rahimi, et al. ∙

research

∙ 03/15/2020

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Predicting calibrated confidence scores for multi-class deep networks is...

5 Amir Rahimi, et al. ∙

research

∙ 12/31/2019

Information Theoretic Model Predictive Q-Learning

Model-free Reinforcement Learning (RL) algorithms work well in sequentia...

29 Mohak Bhardwaj, et al. ∙

research

∙ 12/03/2019

Continuous Online Learning and New Insights to Online Imitation Learning

Online learning is a powerful tool for analyzing iterative algorithms. H...

33 Jonathan Lee, et al. ∙

research

∙ 12/02/2019

Expressiveness and Learning of Hidden Quantum Markov Models

Extending classical probabilistic reasoning using the quantum mechanical...

0 Sandesh Adhikary, et al. ∙

research

∙ 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...

0 Ching-An Cheng, et al. ∙

research

∙ 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...

108 Ajay Mandlekar, et al. ∙

research

∙ 10/07/2019

Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping

RMPflow is a recently proposed policy-fusion framework based on differen...

9 Mustafa Mukadam, et al. ∙

research

∙ 08/08/2019

Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods

Policy gradient methods have demonstrated success in reinforcement learn...

0 Ching-An Cheng, et al. ∙

research

∙ 08/01/2019

Online Motion Planning Over Multiple Homotopy Classes with Gaussian Process Inference

Efficient planning in dynamic and uncertain environments is a fundamenta...

0 Keshav Kolur, et al. ∙

research

∙ 07/22/2019

Differentiable Gaussian Process Motion Planning

Modern trajectory optimization based approaches to motion planning are f...

0 Mohak Bhardwaj, et al. ∙

research

∙ 07/16/2019

Leveraging Experience in Lazy Search

Lazy graph search algorithms are efficient at solving motion planning pr...

0 Mohak Bhardwaj, et al. ∙

research

∙ 05/27/2019

Provably Efficient Imitation Learning from Observation Alone

We study Imitation Learning (IL) from Observations alone (ILFO) in large...

2 Wen Sun, et al. ∙

Byron Boots

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