21 citations · 27 across the 30 of their papers we have counts for
14 papers · 1 filter
Studying the Effect of Explicit Interaction Representations on Learning Scene-level Distributions of Human Trajectories
Anna Mészáros, Javier Alonso-Mora, Jens Kober
Effectively capturing the joint distribution of all agents in a scene is relevant for predicting the true evolution of the scene and in turn providing more accurate information to…
Controllable Generative Trajectory Prediction via Weak Preference Alignment
Yongxi Cao, Julian F. Schumann, Jens Kober +2
Deep generative models such as conditional variational autoencoders (CVAEs) have shown great promise for predicting trajectories of surrounding agents in autonomous vehicle plannin…
Sequentially Teaching Sequential Tasks : Teaching Robots Long-horizon Manipulation Skills
Zlatan Ajanović, Ravi Prakash, Leandro de Souza Rosa +1
Learning from demonstration has proved itself useful for teaching robots complex skills with high sample efficiency. However, teaching long-horizon tasks with multiple skills is ch…
STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction models
Julian F. Schumann, Anna Mészáros, Jens Kober +1
While trajectory prediction plays a critical role in enabling safe and effective path-planning in automated vehicles, standardized practices for evaluating such models remain under…
LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning
Jelle Luijkx, Runyu Ma, Zlatan Ajanović +1
Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive exploration of large state-action…
Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks
Amin Berjaoui Tahmaz, Ravi Prakash, Jens Kober
This paper presents an Impedance Primitive-augmented hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this…
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning
Jelle Luijkx, Zlatan Ajanović, Laura Ferranti +1
Human teaching effort is a significant bottleneck for the broader applicability of interactive imitation learning. To reduce the number of required queries, existing methods employ…
Mode Collapse Happens: Evaluating Critical Interactions in Joint Trajectory Prediction Models
Maarten Hugenholtz, Anna Meszaros, Jens Kober +1
Autonomous Vehicle decisions rely on multimodal prediction models that account for multiple route options and the inherent uncertainty in human behavior. However, models can suffer…
Active inference as a unified model of collision avoidance behavior in human drivers
Julian F. Schumann, Johan Engström, Leif Johnson +4
Collision avoidance -- involving a rapid threat detection and quick execution of the appropriate evasive maneuver -- is a critical aspect of driving. However, existing models of hu…
Versatile, Robust, and Explosive Locomotion with Rigid and Articulated Compliant Quadrupeds
Jiatao Ding, Peiyu Yang, Fabio Boekel +4
Achieving versatile and explosive motion with robustness against dynamic uncertainties is a challenging task. Introducing parallel compliance in quadrupedal design is deemed to enh…
MUKCa: Accurate and Affordable Cobot Calibration Without External Measurement Devices
Giovanni Franzese, Max Spahn, Jens Kober +1
To increase the reliability of collaborative robots in performing daily tasks, we require them to be accurate and not only repeatable. However, having a calibrated kinematics model…
Explosive Jumping with Rigid and Articulated Soft Quadrupeds via Example Guided Reinforcement Learning
Georgios Apostolides, Wei Pan, Jens Kober +2
Achieving controlled jumping behaviour for a quadruped robot is a challenging task, especially when introducing passive compliance in mechanical design. This study addresses this c…
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation
Anish Abhijit Diwan, Julen Urain, Jens Kober +1
This paper introduces a new imitation learning framework based on energy-based generative models capable of learning complex, physics-dependent, robot motion policies through state…
From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback
Zhaoting Li, Rodrigo Pérez-Dattari, Robert Babuska +2
Behavior cloning (BC) optimizes policies by treating human demonstrations as pointwise action labels. While effective with accurate action labels, this formulation is brittle in pr…