37 citations · 142 across the 32 of their papers we have counts for
34 papers
PUMA: Deep Metric Imitation Learning for Stable Motion Primitives
Rodrigo Pérez-Dattari, Cosimo Della Santina, Jens Kober
Imitation Learning (IL) is a powerful technique for intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of rea…
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks
Antonin Raffin, Olivier Sigaud, Jens Kober +3
In search of a simple baseline for Deep Reinforcement Learning in locomotion tasks, we propose a model-free open-loop strategy. By leveraging prior knowledge and the elegance of si…
Two-Stage Learning of Highly Dynamic Motions with Rigid and Articulated Soft Quadrupeds
Francecso Vezzi, Jiatao Ding, Antonin Raffin +2
Controlled execution of dynamic motions in quadrupedal robots, especially those with articulated soft bodies, presents a unique set of challenges that traditional methods struggle…
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study
Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober +2
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. Reliable m…
Quadratic Programming-based Reference Spreading Control for Dual-Arm Robotic Manipulation with Planned Simultaneous Impacts
Jari van Steen, Gijs van den Brandt, Nathan van de Wouw +2
With the aim of further enabling the exploitation of intentional impacts in robotic manipulation, a control framework is presented that directly tackles the challenges posed by tra…
TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction
Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora +2
Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this…
Robotic Packaging Optimization with Reinforcement Learning
Eveline Drijver, Rodrigo Pérez-Dattari, Jens Kober +2
Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times. This work…
Learning from Few Demonstrations with Frame-Weighted Motion Generation
Jianyong Sun, Jens Kober, Michael Gienger +1
Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to t…
Robotic Fabric Flattening with Wrinkle Direction Detection
Yulei Qiu, Jihong Zhu, Cosimo Della Santina +2
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…
Probabilistic Risk Assessment for Chance-Constrained Collision Avoidance in Uncertain Dynamic Environments
Khaled A. Mustafa, Oscar de Groot, Xinwei Wang +2
Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot's mission without incurring…
An Incremental Inverse Reinforcement Learning Approach for Motion Planning with Separated Path and Velocity Preferences
Armin Avaei, Linda van der Spaa, Luka Peternel +1
Humans often demonstrate diverse behaviors due to their personal preferences, for instance, related to their individual execution style or personal margin for safety. In this paper…
Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme
Jihong Zhu, Michael Gienger, Giovanni Franzese +1
Developing physically assistive robots capable of dressing assistance has the potential to significantly improve the lives of the elderly and disabled population. However, most rob…
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti +1
Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses).…
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
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. Accurate and reliable prediction mode…
Interactive Imitation Learning in Robotics: A Survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari +7
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of…
Interactive Imitation Learning of Bimanual Movement Primitives
Giovanni Franzese, Leandro de Souza Rosa, Tim Verburg +2
Performing bimanual tasks with dual robotic setups can drastically increase the impact on industrial and daily life applications. However, performing a bimanual task brings many ch…
Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field
Yurui Du, Flavia Sofia Acerbo, Jens Kober +1
In recent years, imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. However, previous IL works show sample inefficien…
Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration
Mariano Ramirez Montero, Giovanni Franzese, Jeroen Zwanepol +1
Many high precision (dis)assembly tasks are still being performed by humans, whereas this is an ideal opportunity for automation. This paper provides a framework which enables a no…
Learning to Exploit Elastic Actuators for Quadruped Locomotion
Antonin Raffin, Daniel Seidel, Jens Kober +3
Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design. While previous work has focused on…
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics
N. Passalis, S. Pedrazzi, R. Babuska +15
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their r…
Learning Task-Parameterized Skills from Few Demonstrations
Jihong Zhu, Michael Gienger, Jens Kober
Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion pol…
Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations
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. Due to the complexity of…
Challenges and Outlook in Robotic Manipulation of Deformable Objects
Jihong Zhu, Andrea Cherubini, Claire Dune +10
Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new a…
ILoSA: Interactive Learning of Stiffness and Attractors
Giovanni Franzese, Anna Mészáros, Luka Peternel +1
Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to…