Search Results for author:
Found 12 papers, 3 papers with code
Automated Gait Generation For Walking, Soft Robotic Quadrupeds
Gait generation for soft robots is challenging due to the nonlinear dynamics and high dimensional input spaces of soft actuators.
Maximum diffusion reinforcement learning
The assumption that data are independent and identically distributed underpins all machine learning.
Dynamical System Segmentation for Information Measures in Motion
For a given task, we specify an optimal agent, and compute an alphabet of behaviors representative of the task.
Robotics
Learning from Human Directional Corrections
This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections.
Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems
This paper presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states.
Learning from Sparse Demonstrations
The time stamps of the keyframes can be different from the time of the robot's actual execution.
An Ergodic Measure for Active Learning From Equilibrium
We show that our method is able to maintain Lyapunov attractiveness with respect to the equilibrium task while actively generating data for learning tasks such, as Bayesian optimization, model learning, and off-policy reinforcement learning.
Active Learning
Robotics
Active Area Coverage from Equilibrium
This paper develops a method for robots to integrate stability into actively seeking out informative measurements through coverage.
Robotics
Decentralized Ergodic Control: Distribution-Driven Sensing and Exploration for Multi-Agent Systems
We present a decentralized ergodic control policy for time-varying area coverage problems for multiple agents with nonlinear dynamics.
Robotics Systems and Control
Data-Driven Measurement Models for Active Localization in Sparse Environments
Exploration with respect to the information density based on the data-driven measurement model enables localization.
Robotics
Ergodic Exploration using Binary Sensing for Non-Parametric Shape Estimation
Current methods to estimate object shape---using either vision or touch---generally depend on high-resolution sensing.
Robotics
Real-Time Area Coverage and Target Localization using Receding-Horizon Ergodic Exploration
Although a number of solutions exist for the problems of coverage, search and target localization---commonly addressed separately---whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question.
Robotics
