Search Results for author:
Found 6 papers, 1 papers with code
Human Motor Learning Dynamics in High-dimensional Tasks
Conventional approaches to enhancing movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs).
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot
In this paper, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms.
Label-Efficient Learning in Agriculture: A Comprehensive Review
In addition, a systematic review of various agricultural applications exploiting these label-efficient algorithms, such as precision agriculture, plant phenotyping, and postharvest quality assessment, is presented.
Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces
Designing effective rehabilitation strategies for upper extremities, particularly hands and fingers, warrants the need for a computational model of human motor learning.
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.
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes
Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection.
