Search Results for author:
Found 9 papers, 1 papers with code
A Vision-Guided Robotic System for Grasping Harvested Tomato Trusses in Cluttered Environments
The method consists of a deep learning-based vision system to first identify the individual trusses in the crate and then determine a suitable grasping location on the stem.
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
This paper introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera.
Geometry-Based Grasping of Vine Tomatoes
The grasping method then uses a geometric model of the robotic hand and the truss to determine a suitable grasping location on the stem.
GEM: Glare or Gloom, I Can Still See You -- End-to-End Multimodal Object Detection
We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions.
DeepKoCo: Efficient latent planning with a task-relevant Koopman representation
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images.
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice
A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation.
Residential Demand Response Applications Using Batch Reinforcement Learning
We propose a model-free Monte-Carlo estimator method that uses a metric to construct artificial trajectories and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat.
Sampling-based Approximations with Quantitative Performance for the Probabilistic Reach-Avoid Problem over General Markov Processes
This article deals with stochastic processes endowed with the Markov (memoryless) property and evolving over general (uncountable) state spaces.
Reinforcement learning for port-Hamiltonian systems
The parameters of the control law are found using actor-critic reinforcement learning, enabling learning near-optimal control policies satisfying a desired closed-loop energy landscape.
