no code implementations • 19 Jan 2024 • Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, Jens Kober
Development of multi-modal, probabilistic prediction models has lead to a need for comprehensive evaluation metrics.
1 code implementation • 30 Nov 2023 • Daniel Jarne Ornia, Giannis Delimpaltadakis, Jens Kober, Javier Alonso-Mora
In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e. g. policy entropy regularization) to randomize their actions in favor of exploration.
no code implementations • 24 May 2023 • Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober, Gustav Markkula, Arkady Zgonnikov
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style.
no code implementations • 26 Mar 2023 • Eveline Drijver, Rodrigo Pérez-Dattari, Jens Kober, Cosimo Della Santina, Zlatan Ajanović
Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times.
no code implementations • 8 Mar 2023 • Yulei Qiu, Jihong Zhu, Cosimo Della Santina, Michael Gienger, Jens Kober
Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc.
no code implementations • 15 Nov 2022 • Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti, Jens Kober
We extend previous works and present the PARTNR algorithm that can detect ambiguities in the trained policy by analyzing multiple modalities in the pick and place poses using topological analysis.
no code implementations • 10 Nov 2022 • Julian Frederik Schumann, Jens Kober, Arkady Zgonnikov
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions.
no code implementations • 4 Oct 2022 • Yurui Du, Flavia Sofia Acerbo, Jens Kober, Tong Duy Son
To continuously improve the learnt policy, we retrain the IL model with augmented data.
no code implementations • 15 Sep 2022 • Antonin Raffin, Daniel Seidel, Jens Kober, Alin Albu-Schäffer, João Silvério, Freek Stulp
Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design.
1 code implementation • 9 Oct 2021 • Anna Mészáros, Giovanni Franzese, Jens Kober
This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections.
1 code implementation • 4 Mar 2021 • Giovanni Franzese, Anna Mészáros, Luka Peternel, Jens Kober
Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives.
no code implementations • 24 Feb 2021 • Osama Mazhar, Robert Babuska, Jens Kober
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.
1 code implementation • 13 Jan 2021 • Osama Mazhar, Jens Kober
In this paper, we propose a new data augmentation method, Random Shadows and Highlights (RSH) to acquire robustness against lighting perturbations.
no code implementations • 25 Nov 2020 • Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuska
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images.
1 code implementation • 2 Aug 2020 • Snehal Jauhri, Carlos Celemin, Jens Kober
Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering.
4 code implementations • 12 May 2020 • Antonin Raffin, Jens Kober, Freek Stulp
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
no code implementations • 14 Aug 2019 • Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober
However, DRL has several limitations when used in real-world problems (e. g., robotics applications).
2 code implementations • 14 Mar 2019 • Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober
We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning.
no code implementations • 12 Mar 2019 • Daan Wout, Jan Scholten, Carlos Celemin, Jens Kober
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.
1 code implementation • 30 Sep 2018 • Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).
no code implementations • NeurIPS 2008 • Jens Kober, Jan R. Peters
We compare this algorithm to alternative parametrized policy search methods and show that it outperforms previous methods.
no code implementations • NeurIPS 2008 • Silvia Chiappa, Jens Kober, Jan R. Peters
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.