Torsten Kröger
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Safe Reinforcement Learning of Robot Trajectories in the Presence of Moving Obstacles Open
In this paper, we present an approach for learning collision-free robot trajectories in the presence of moving obstacles. As a first step, we train a backup policy to generate evasive movements from arbitrary initial robot states using mod…
Planning with Learned Subgoals Selected by Temporal Information Open
Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate …
HIRO: Heuristics Informed Robot Online Path Planning Using Pre-computed Deterministic Roadmaps Open
With the goal of efficiently computing collision-free robot motion trajectories in dynamically changing environments, we present results of a novel method for Heuristics Informed Robot Online Path Planning (HIRO). Dividing robot environmen…
Jerk-limited Traversal of One-dimensional Paths and its Application to Multi-dimensional Path Tracking Open
In this paper, we present an iterative method to quickly traverse multi-dimensional paths considering jerk constraints. As a first step, we analyze the traversal of each individual path dimension. We derive a range of feasible target accel…
Reinforcement Learning for Safety Testing: Lessons from A Mobile Robot Case Study Open
Safety-critical robot systems need thorough testing to expose design flaws and software bugs which could endanger humans. Testing in simulation is becoming increasingly popular, as it can be applied early in the development process and doe…
Uncertainty-aware Risk Assessment of Robotic Systems via Importance Sampling Open
In this paper, we introduce a probabilistic approach to risk assessment of robot systems by focusing on the impact of uncertainties. While various approaches to identifying systematic hazards (e.g., bugs, design flaws, etc.) can be found i…
Safety Evaluation of Robot Systems via Uncertainty Quantification Open
In this paper, we present an approach for quantifying the propagated uncertainty of robot systems in an online and data-driven manner. Especially in Human-Robot Collaboration, keeping track of the safety compliance during run time is essen…
Hazard Analysis of Collaborative Automation Systems: A Two-layer Approach based on Supervisory Control and Simulation Open
Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reason…
Uncertainty Estimation for Safe Human-Robot Collaboration using Conservation Measures Open
We present an online and data-driven uncertainty quantification method to enable the development of safe human-robot collaboration applications. Safety and risk assessment of systems are strongly correlated with the accuracy of measurement…
SpeedFolding: Learning Efficient Bimanual Folding of Garments Open
Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments. An intuitive approach is to initially manipulate the garment t…
Learning Time-optimized Path Tracking with or without Sensory Feedback Open
In this paper, we present a learning-based approach that allows a robot to quickly follow a reference path defined in joint space without exceeding limits on the position, velocity, acceleration and jerk of each robot joint. Contrary to of…
Testing Robot System Safety by creating Hazardous Human Worker Behavior in Simulation Open
We introduce a novel simulation-based approach to identify hazards that result from unexpected worker behavior in human-robot collaboration. Simulation-based safety testing must take into account the fact that human behavior is variable an…
Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation Open
Robot learning of real-world manipulation tasks remains challenging and time consuming, even though actions are often simplified by single-step manipulation primitives. In order to compensate the removed time dependency, we additionally le…
Learning a Generative Transition Model for Uncertainty-Aware Robotic\n Manipulation Open
Robot learning of real-world manipulation tasks remains challenging and time\nconsuming, even though actions are often simplified by single-step manipulation\nprimitives. In order to compensate the removed time dependency, we additionally\…
Jerk-limited Real-time Trajectory Generation with Arbitrary Target States Open
We present Ruckig, an algorithm for Online Trajectory Generation (OTG) respecting third-order constraints and complete kinematic target states. Given any initial state of a system with multiple Degrees of Freedom (DoFs), Ruckig calculates …
Learning Robot Trajectories subject to Kinematic Joint Constraints Open
We present an approach to learn fast and dynamic robot motions without exceeding limits on the position $θ$, velocity $\dotθ$, acceleration $\ddotθ$ and jerk $\dddotθ$ of each robot joint. Movements are generated by mapping the predictions…
Robot Learning of 6 DoF Grasping using Model-based Adaptive Primitives Open
Robot learning is often simplified to planar manipulation due to its data consumption. Then, a common approach is to use a fully-convolutional neural network to estimate the reward of grasp primitives. In this work, we extend this approach…
Virtual Adversarial Humans finding Hazards in Robot Workplaces Open
During the planning phase of industrial robot workplaces, hazard analyses are required so that potential hazards for human workers can be identified and appropriate safety measures can be implemented. Existing hazard analysis methods use h…
Jerk-limited Real-time Trajectory Generation with Arbitrary Target\n States Open
We present Ruckig, an algorithm for Online Trajectory Generation (OTG)\nrespecting third-order constraints and complete kinematic target states. Given\nany initial state of a system with multiple Degrees of Freedom (DoFs), Ruckig\ncalculat…
Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors Open
This paper presents an approach for learning online generation of collision-free and torque-limited robot trajectories. In order to generate future motions, a neural network is periodically invoked. Based on the current kinematic state of …
Simulation-based Testing for Early Safety-Validation of Robot Systems Open
Industrial human-robot collaborative systems must be validated thoroughly with regard to safety. The sooner potential hazards for workers can be exposed, the less costly is the implementation of necessary changes. Due to the complexity of …
Self-Supervised Learning for Precise Pick-and-Place Without Object Model Open
Flexible pick-and-place is a fundamental yet challenging task within\nrobotics, in particular due to the need of an object model for a simple target\npose definition. In this work, the robot instead learns to pick-and-place\nobjects using …
Self-supervised Learning for Precise Pick-and-place without Object Model Open
Flexible pick-and-place is a fundamental yet challenging task within robotics, in particular due to the need of an object model for a simple target pose definition. In this work, the robot instead learns to pick-and-place objects using pla…
True{\AE}dapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space Open
We present True{\AE}dapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained …
TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space Open
We present TrueÆdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to p…
TrueRMA: Learning Fast and Smooth Robot Trajectories with Recursive Midpoint Adaptations in Cartesian Space Open
We present TrueRMA, a data-efficient, model-free method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate trajectory waypoints in Cartesian space by recursively pred…
Robot Learning of Shifting Objects for Grasping in Cluttered Environments Open
Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pre-grasping manipulation like shifting or pushing an object becomes necessary. We developed an algorithm that can learn, in …
General Hand Guidance Framework using Microsoft HoloLens Open
Hand guidance emerged from the safety requirements for collaborative robots, namely possessing joint-torque sensors. Since then it has proven to be a powerful tool for easy trajectory programming, allowing lay-users to reprogram robots int…
Robot Learning of Shifting Objects for Grasping in Cluttered\n Environments Open
Robotic grasping in cluttered environments is often infeasible due to\nobstacles preventing possible grasps. Then, pre-grasping manipulation like\nshifting or pushing an object becomes necessary. We developed an algorithm that\ncan learn, …
Robotics Education and Research at Scale: A Remotely Accessible Robotics Development Platform Open
This paper introduces the KUKA Robot Learning Lab at KIT - a remotely accessible robotics testbed. The motivation behind the laboratory is to make state-of-the-art industrial lightweight robots more accessible for education and research. S…