Search Results for author:
Found 12 papers, 8 papers with code
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state.
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions.
MP3: Movement Primitive-Based (Re-)Planning Policy
We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3).
Curriculum-Based Imitation of Versatile Skills
Learning skills by imitation is a promising concept for the intuitive teaching of robots.
Goal-Conditioned Imitation Learning using Score-based Diffusion Policies
To the best of our knowledge this is the first work that a) represents a behavior policy based on such a decoupled SDM b) learns an SDM based policy in the domain of GCIL and c) provides a way to simultaneously learn a goal-dependent and a goal-independent policy from play-data.
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills
Imitation learning uses data for training policies to solve complex tasks.
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives
MPs can be broadly categorized into two types: (a) dynamics-based approaches that generate smooth trajectories from any initial state, e. g., Dynamic Movement Primitives (DMPs), and (b) probabilistic approaches that capture higher-order statistics of the motion, e. g., Probabilistic Movement Primitives (ProMPs).
Distributional Depth-Based Estimation of Object Articulation Models
Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds.
Self-Supervised Online Reward Shaping in Sparse-Reward Environments
We introduce Self-supervised Online Reward Shaping (SORS), which aims to improve the sample efficiency of any RL algorithm in sparse-reward environments by automatically densifying rewards.
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to-day tasks.
Probabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems
Using movement primitive libraries is an effective means to enable robots to solve more complex tasks.
Model-Based Relative Entropy Stochastic Search
Stochastic search algorithms are general black-box optimizers.
