117 citations · 239 across the 13 of their papers we have counts for
13 papers · 1 filter
Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control
Claudio Canales, Fang Nan, Marco Hutter +1
Precise, high-speed control remains challenging for robots with complex actuation dynamics. Learning directly on hardware is further constrained by the cost of real-world interacti…
Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot
Ethan Marot, Thomas Bi, Clemens Schwarke +3
As humanoid robots become increasingly dynamic, coupling them with reinforcement learning offers a promising approach to solving the complex, underactuated mechanics of passive inl…
System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robot
Benjamin Bokser, Daniel Gonzalez, Aaron Preston +39
Trials cyclists and mountain bike riders can hop, jump, balance, and drive on one or both wheels. This versatility allows them to achieve speed and energy-efficiency on smooth terr…
Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Junzhe He, Chong Zhang, Fabian Jenelten +3
Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds ar…
DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
Joshua Bagajo, Clemens Schwarke, Victor Klemm +5
Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from…
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation
Jean-Pierre Sleiman, Mayank Mittal, Marco Hutter
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a syst…
Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation
Jing Yuan Luo, Yunlong Song, Victor Klemm +3
First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, si…
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots
Joonho Lee, Marko Bjelonic, Alexander Reske +3
Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban enviro…
Learning Deployable Locomotion Control via Differentiable Simulation
Clemens Schwarke, Victor Klemm, Joshua Bagajo +4
Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich task…
Symmetry Considerations for Learning Task Symmetric Robot Policies
Mayank Mittal, Nikita Rudin, Victor Klemm +2
Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively…
Solving Multi-Entity Robotic Problems Using Permutation Invariant Neural Networks
Tianxu An, Joonho Lee, Marko Bjelonic +2
Challenges in real-world robotic applications often stem from managing multiple, dynamically varying entities such as neighboring robots, manipulable objects, and navigation goals.…
Evaluation of Constrained Reinforcement Learning Algorithms for Legged Locomotion
Joonho Lee, Lukas Schroth, Victor Klemm +3
Shifting from traditional control strategies to Deep Reinforcement Learning (RL) for legged robots poses inherent challenges, especially when addressing real-world physical constra…
DTC: Deep Tracking Control
Fabian Jenelten, Junzhe He, Farbod Farshidian +1
Legged locomotion is a complex control problem that requires both accuracy and robustness to cope with real-world challenges. Legged systems have traditionally been controlled usin…