1 code implementation • 12 Dec 2024 • Ralf Römer, Alexander von Rohr, Angela P. Schoellig
Diffusion models have recently gained popularity for policy learning in robotics due to their ability to capture high-dimensional and multimodal distributions.
no code implementations • 11 Dec 2024 • Vedant Vyas, Martin Schuck, Dinushka O. Dahanaggamaarachchi, SiQi Zhou, Angela P. Schoellig
Catalyzed by advancements in hardware and software, drone performances are increasingly making their mark in the entertainment industry.
no code implementations • 5 Dec 2024 • Alan Li, Angela P. Schoellig
6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment.
1 code implementation • 15 Oct 2024 • Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig
The modifications allow the RL controller to learn to account for the safety filter, improving performance.
1 code implementation • 17 Sep 2024 • Lukas Brunke, SiQi Zhou, Angela P. Schoellig
In real-world applications, continuous-time CBF safety filters are implemented in discrete time, exacerbating issues related to violating the condition on the relative degree.
no code implementations • 18 Apr 2024 • Lukas Brunke, SiQi Zhou, Mingxuan Che, Angela P. Schoellig
In particular, we look at the issues caused by discrete-time implementations of the continuous-time CBF-based safety filter, especially for cases where the magnitude of the Lie derivative of the CBF with respect to the control input is zero or close to zero.
1 code implementation • 14 Mar 2024 • Ralf Römer, Lukas Brunke, SiQi Zhou, Angela P. Schoellig
While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance.
no code implementations • 15 Dec 2023 • Lukas Brunke, SiQi Zhou, Mingxuan Che, Angela P. Schoellig
We demonstrate the efficacy of our proposed approach in simulation and real-world experiments on a quadrotor and show that we can achieve safe closed-loop behavior for a learned system while satisfying state and input constraints.
1 code implementation • 20 Sep 2023 • Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig
In experiments with a Crazyflie 2. 0 drone, we show that, in addition to preserving the desired safety guarantees, the proposed MPSF reduces chattering by more than a factor of 4 compared to previous MPSF formulations.
2 code implementations • 17 Sep 2023 • Ziwei Liao, Jun Yang, Jingxing Qian, Angela P. Schoellig, Steven L. Waslander
Unlike current state-of-the-art approaches, we explicitly model the uncertainty of the object shapes and poses during our optimization, resulting in a high-quality object-level mapping system.
1 code implementation • 19 Aug 2023 • SiQi Zhou, Lukas Brunke, Allen Tao, Adam W. Hall, Federico Pizarro Bejarano, Jacopo Panerati, Angela P. Schoellig
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community.
1 code implementation • 20 Jul 2023 • Adam W. Hall, Melissa Greeff, Angela P. Schoellig
This safety filter is then used to refine inputs from a flat model predictive controller to perform constrained nonlinear learning-based optimal control through two successive convex optimizations.
no code implementations • 29 Mar 2023 • Alan Li, Angela P. Schoellig
6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment.
1 code implementation • 17 Dec 2022 • Lukas Brunke, SiQi Zhou, Angela P. Schoellig
Recently, we have seen an increasing number of learning-based control algorithms developed to address the challenge of decision making under dynamics uncertainties.
no code implementations • 7 Dec 2021 • SiQi Zhou, Karime Pereida, Wenda Zhao, Angela P. Schoellig
In particular, we present a learning-based model reference adaptation approach that makes a robot system, with possibly uncertain dynamics, behave as a predefined reference model.
no code implementations • 1 Oct 2021 • Lukas Brunke, SiQi Zhou, Angela P. Schoellig
In this work we address the problem of performing a repetitive task when we have uncertain observations and dynamics.
4 code implementations • 13 Sep 2021 • Zhaocong Yuan, Adam W. Hall, SiQi Zhou, Lukas Brunke, Melissa Greeff, Jacopo Panerati, Angela P. Schoellig
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained significant traction.
4 code implementations • 13 Aug 2021 • Lukas Brunke, Melissa Greeff, Adam W. Hall, Zhaocong Yuan, SiQi Zhou, Jacopo Panerati, Angela P. Schoellig
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities.
2 code implementations • 3 Mar 2021 • Jacopo Panerati, Hehui Zheng, SiQi Zhou, James Xu, Amanda Prorok, Angela P. Schoellig
Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications.
1 code implementation • 2 Mar 2021 • Wenda Zhao, Jacopo Panerati, Angela P. Schoellig
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks.
no code implementations • 29 Mar 2020 • Michael J. Sorocky, Siqi Zhou, Angela P. Schoellig
We show that selecting experiences based on the proposed similarity metric effectively facilitates the learning of the target quadrotor, improving performance by 62% compared to a poorly selected experience.
no code implementations • 21 Mar 2020 • Jeremy N. Wong, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot
Our contribution is to additionally learn parameters of our system models (which may be difficult to choose in practice) within the ESGVI framework.
no code implementations • 20 Mar 2020 • Wenda Zhao, Abhishek Goudar, Jacopo Panerati, Angela P. Schoellig
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks.
no code implementations • 17 Mar 2020 • Ke Dong, Karime Pereida, Florian Shkurti, Angela P. Schoellig
Typically, mobile manipulators are deployed in slow-motion collaborative robot scenarios.
no code implementations • 24 Dec 2019 • SiQi Zhou, Angela P. Schoellig
We consider this work to be a step towards understanding the expressive power of DNNs and towards designing appropriate deep architectures for practical applications such as system control.
no code implementations • 10 Jan 2019 • Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
In this paper, we present the first BO algorithm that is provably no-regret and converges to the optimum without knowledge of the hyperparameters.
no code implementations • 3 Nov 2018 • Keenan Burnett, Andreas Schimpe, Sepehr Samavi, Mona Gridseth, Chengzhi Winston Liu, Qiyang Li, Zachary Kroeze, Angela P. Schoellig
The first set of challenges were held in April of 2018 in Yuma, Arizona.
Robotics
no code implementations • 3 Apr 2018 • Mohamed K. Helwa, Adam Heins, Angela P. Schoellig
Inverse dynamics control and feedforward linearization techniques are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-loop controller can be used to calculate the commanded acceleration for the linearized system.
no code implementations • 19 Sep 2017 • Qiyang Li, Xintong Du, Yizhou Huang, Quinlan Sykora, Angela P. Schoellig
Inspired by biological swarms, robotic swarms are envisioned to solve real-world problems that are difficult for individual agents.
no code implementations • 13 Sep 2017 • Siqi Zhou, Mohamed K. Helwa, Angela P. Schoellig
This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i. e., systems with unstable inverse dynamics.
no code implementations • 27 Jul 2017 • Mohamed K. Helwa, Angela P. Schoellig
In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps.
1 code implementation • NeurIPS 2017 • Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data.
no code implementations • 3 Mar 2017 • Alonso Marco, Felix Berkenkamp, Philipp Hennig, Angela P. Schoellig, Andreas Krause, Stefan Schaal, Sebastian Trimpe
In practice, the parameters of control policies are often tuned manually.
no code implementations • 20 Oct 2016 • Qiyang Li, Jingxing Qian, Zining Zhu, Xuchan Bao, Mohamed K. Helwa, Angela P. Schoellig
Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making.
no code implementations • 14 Jul 2016 • Thomas Bamford, Kamran Esmaeili, Angela P. Schoellig
The pile was photographed by a camera attached to the UAV, and the particle size distribution curves were generated in almost real-time.
no code implementations • 18 Mar 2016 • Andreas Hock, Angela P. Schoellig
This is the first work to show distributed ILC in experiment.
2 code implementations • 15 Mar 2016 • Felix Berkenkamp, Riccardo Moriconi, Angela P. Schoellig, Andreas Krause
The ROA is typically estimated based on a model of the system.
Systems and Control
3 code implementations • 14 Feb 2016 • Felix Berkenkamp, Andreas Krause, Angela P. Schoellig
While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually on the real system to achieve the best performance.
3 code implementations • 3 Sep 2015 • Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters.
Robotics