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Found 11 papers, 2 papers with code
Antifragility as a complex system's response to perturbations, volatility, and time
Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations.
Constrained Equation Learner Networks for Precision-Preserving Extrapolation of Robotic Skills
While conventional approaches for constrained regression use one kind of basis function, e. g., Gaussian, we exploit Equation Learner Networks to learn a set of analytical expressions and use them as basis functions.
Affordance detection with Dynamic-Tree Capsule Networks
In the experimental evaluation we will show that our algorithm is superior to current affordance detection methods when faced with grasping previously unseen objects thanks to our Capsule Network enforcing a parts-to-whole representation.
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
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.
Continual Learning from Demonstration of Robotics Skills
We empirically demonstrate the effectiveness of this approach in remembering long sequences of trajectory learning tasks without the need to store any data from past demonstrations.
Periodic DMP formulation for Quaternion Trajectories
While DMPs have been properly formulated for learning point-to-point movements for both translation and orientation, periodic ones are missing a formulation to learn the orientation.
Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions
This paper presents an approach to deal with safety of dynamical systems in presence of multiple non-convex unsafe sets.
How do Offline Measures for Exploration in Reinforcement Learning behave?
Sufficient exploration is paramount for the success of a reinforcement learning agent.
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
Local policy search is performed by most Deep Reinforcement Learning (D-RL) methods, which increases the risk of getting trapped in a local minimum.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Human Action Descriptor Based on Motion Coordination
The second step enriches the descriptor considering minimum and maximum joint velocities and the correlations between the most informative joints.
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation
This way, the occurrence of object slipping during the learning procedure, which we consider an irreversible event, is significantly reduced.
