10 citations · 21 across the 12 of their papers we have counts for
13 papers
Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch
Yoshiki Takebayashi, Giovanni Perantoni, Hikaru Sasaki +2
With the increasing use of robot-free demonstration interfaces that provide state trajectories without action labels, imitation from observation has become a promising approach for…
Embedding Physics Priors in Robot Learning: A Survey
Mattia Piccinini, Lucas Schulze, Alice Plebe +10
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remar…
Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation
Sayantan Auddy, Antonio Paolillo, Justus Piater +1
Today robots must be safe, versatile, and user-friendly to operate in unstructured and human-populated environments. Dynamical system-based imitation learning enables robots to per…
Constrained Equation Learner Networks for Precision-Preserving Extrapolation of Robotic Skills
Hector Perez-Villeda, Justus Piater, Matteo Saveriano
In Programming by Demonstration, the robot learns novel skills from human demonstrations. After learning, the robot should be able not only to reproduce the skill, but also to gene…
Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model
Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano +2
Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world, they should also remember multi…
Imitation Learning-based Visual Servoing for Tracking Moving Objects
Rocco Felici, Matteo Saveriano, Loris Roveda +1
In everyday life collaboration tasks between human operators and robots, the former necessitate simple ways for programming new skills, the latter have to show adaptive capabilitie…
Learning and Extrapolation of Robotic Skills using Task-Parameterized Equation Learner Networks
Hector Villeda, Justus Piater, Matteo Saveriano
Imitation learning approaches achieve good generalization within the range of the training data, but tend to generate unpredictable motions when querying outside this range. We pre…
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
Jakob Hollenstein, Sayantan Auddy, Matteo Saveriano +2
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the sc…
Learning Stable Dynamical Systems for Visual Servoing
Antonio Paolillo, Matteo Saveriano
This work presents the dual benefit of integrating imitation learning techniques, based on the dynamical systems formalism, with the visual servoing paradigm. On the one hand, dyna…
Continual Learning from Demonstration of Robotics Skills
Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano +2
Methods for teaching motion skills to robots focus on training for a single skill at a time. Robots capable of learning from demonstration can considerably benefit from the added a…
Learning Descriptor of Constrained Task from Demonstration
Xiang Zhang, Matteo Saveriano, Justus Piater
Constrained objects, such as doors and drawers are often complex and share a similar structure in the human environment. A robot needs to interact accurately with constrained objec…
How do Offline Measures for Exploration in Reinforcement Learning behave?
Jakob J. Hollenstein, Sayantan Auddy, Matteo Saveriano +2
Sufficient exploration is paramount for the success of a reinforcement learning agent. Yet, exploration is rarely assessed in an algorithm-independent way. We compare the behavior…
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
Jakob J. Hollenstein, Erwan Renaudo, Matteo Saveriano +1
Local policy search is performed by most Deep Reinforcement Learning (D-RL) methods, which increases the risk of getting trapped in a local minimum. Furthermore, the availability o…