62 citations · 149 across the 19 of their papers we have counts for
9 papers · 1 filter
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications
Puze Liu, Haitham Bou-Ammar, Jan Peters +1
Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. However, most existi…
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
Luca Lach, Robert Haschke, Davide Tateo +4
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation ta…
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Piotr Kicki, Puze Liu, Davide Tateo +4
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion…
Learning-based Design and Control for Quadrupedal Robots with Parallel-Elastic Actuators
Filip Bjelonic, Joonho Lee, Philip Arm +4
Parallel-elastic joints can improve the efficiency and strength of robots by assisting the actuators with additional torques. For these benefits to be realized, a spring needs to b…
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction
Puze Liu, Kuo Zhang, Davide Tateo +4
Safety is a crucial property of every robotic platform: any control policy should always comply with actuator limits and avoid collisions with the environment and humans. In reinfo…
Regularized Deep Signed Distance Fields for Reactive Motion Generation
Puze Liu, Kuo Zhang, Davide Tateo +3
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and man…
Learning Stable Vector Fields on Lie Groups
Julen Urain, Davide Tateo, Jan Peters
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in re…
Efficient and Reactive Planning for High Speed Robot Air Hockey
Puze Liu, Davide Tateo, Haitham Bou-Ammar +1
Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high un…
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
Julen Urain, Davide Tateo, Tianyu Ren +1
We present a new family of deep neural network-based dynamic systems. The presented dynamics are globally stable and can be conditioned with an arbitrary context state. We show how…