Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives.
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.
In this paper, we propose a new data augmentation method, Random Shadows and Highlights (RSH) to acquire robustness against lighting perturbations.
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images.
We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car.
Ranked #1 on Continuous Control on PyBullet HalfCheetah
However, DRL has several limitations when used in real-world problems (e. g., robotics applications).
We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning.
We demonstrate that the novel algorithm outperforms the current state-of-the-art in final performance, convergence rate and robustness to erroneous feedback in OpenAI Gym continuous control benchmarks, both for simulated and real human teachers.
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).
Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning.