Author name cluster

Julius Jankowski

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics

Puze Liu
Jonas Günster
Niklas Funk
Simon Gröger
Dong Chen
Haitham Bou-Ammar
Julius Jankowski
Ante Marić

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real robots, extra effort is required to address the challenges posed by various real-world factors. To investigate the key factors influencing real-world deployment and to encourage original solutions from different researchers, we organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We selected the air hockey task as a benchmark, encompassing low-level robotics problems and high-level tactics. Different from other machine learning-centric benchmarks, participants need to tackle practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Furthermore, we focus on a dynamic environment, removing the typical assumption of quasi-static motions of other real-world benchmarks. The competition's results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging. Our ablation study reveals which real-world factors may be overlooked when building a learning-based solution. The successful real-world air hockey deployment of best-performing agents sets the foundation for future competitions and follow-up research directions.

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IROS Conference 2023 Conference Paper

A Multitask and Kernel Approach for Learning to Push Objects with a Target-Parameterized Deep Q-Network

Marco Ewerton
Michael Villamizar
Julius Jankowski
Sylvain Calinon
Jean-Marc Odobez

Pushing is an essential motor skill involved in several manipulation tasks, and has been an important research topic in robotics. Recent works have shown that Deep Q-Networks (DQNs) can learn pushing policies (when, where to push, and how) to solve manipulation tasks, potentially in synergy with other skills (e. g. grasping). Nevertheless, DQNs often assume a fixed setting and task, which may limit their deployment in practice. Furthermore, they suffer from sparse-gradient backpropagation when the action space is very large, a problem exacerbated by the fact that they are trained to predict state-action values based on a single reward function aggregating several facets of the task, rendering the model training challenging. To address these issues, we propose a multi-head target-parameterized DQN to learn robotic manipulation tasks, in particular pushing policies, and make the following contributions: i) we show that learning to predict different reward and task aspects can be beneficial compared to predicting a single value function where reward factors are not disentangled; ii) we study several alternatives to generalize a policy by encoding the target parameters either into the network layers or visually in the input; iii) we propose a kernelized version of the loss function, allowing to obtain better, faster and more stable training performance. Extensive experiments on simulations validate our design choices, and we show that our architecture learned on simulated data can achieve high performance in a real-robot setup involving a Franka Emika robot arm and unseen objects.

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ICRA Conference 2023 Conference Paper

VP-STO: Via-point-based Stochastic Trajectory Optimization for Reactive Robot Behavior

Julius Jankowski
Lara Brudermüller
Nick Hawes
Sylvain Calinon

Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high for the task at hand. We argue that current limitations in Model Predictive Control (MPC) for robot manipulators arise from inefficient, high-dimensional trajectory representations and the negligence of time-optimality in the trajectory optimization process. Therefore, we propose a motion optimization framework that optimizes jointly over space and time, generating smooth and timing-optimal robot trajectories in joint-space. While being task-agnostic, our formulation can incorporate additional task-specific requirements, such as collision avoidance, and yet maintain real-time control rates, demonstrated in simulation and real-world robot experiments on closed-loop manipulation. For additional material, please visit https://sites.google.com/oxfordrobotics.institute/vp-sto.

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ICRA Conference 2019 Conference Paper

Sliding Mode Momentum Observers for Estimation of External Torques and Joint Acceleration

Gianluca Garofalo
Nico Mansfeld
Julius Jankowski
Christian Ott 0001

Interactions between robots and their environment give rise to external wrenches acting on the robot structure. The estimation of the resulting torques in the joints is fundamental in human-robot interaction to detect/identify collisions and perform suitable reaction strategies. Other applications may require to use the estimation for compensating the effects of the external torques within the control loop. The well-established momentum observer, which relies on proprioceptive sensors only, is usually used for these purposes. In this work, the momentum dynamics is used to derive new observers. While the classic momentum observer provides a first-order filtered version of the external torques, here a (theoretically) finite-time convergence is achieved. Simulations and experiments are used to validate the performance of the proposed methods.

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