Euclideanizing flows: Diffeomorphic reduction for learning stable dynamical systems
Execution of complex tasks in robotics requires motions that have complex geometric
structure. We present an approach which allows robots to learn such motions from a few …
structure. We present an approach which allows robots to learn such motions from a few …
Neural geometric fabrics: Efficiently learning high-dimensional policies from demonstration
Learning dexterous manipulation policies for multi-fingered robots has been a long-standing
challenge in robotics. Existing methods either limit themselves to highly constrained …
challenge in robotics. Existing methods either limit themselves to highly constrained …
Temporal logic imitation: Learning plan-satisficing motion policies from demonstrations
Learning from demonstration (LfD) has succeeded in tasks featuring a long time horizon.
However, when the problem complexity also includes human-in-the-loop perturbations, state …
However, when the problem complexity also includes human-in-the-loop perturbations, state …
On the utility of koopman operator theory in learning dexterous manipulation skills
Despite impressive dexterous manipulation capabilities enabled by learning-based
approaches, we are yet to witness widespread adoption beyond well-resourced …
approaches, we are yet to witness widespread adoption beyond well-resourced …
RMPflow: A Geometric Framework for Generation of Multitask Motion Policies
Generating robot motion for multiple tasks in dynamic environments is challenging, requiring
an algorithm to respond reactively while accounting for complex nonlinear relationships …
an algorithm to respond reactively while accounting for complex nonlinear relationships …
Motion planning by learning the solution manifold in trajectory optimization
T Osa - The International Journal of Robotics Research, 2022 - journals.sagepub.com
The objective function used in trajectory optimization is often non-convex and can have an
infinite set of local optima. In such cases, there are diverse solutions to perform a given task …
infinite set of local optima. In such cases, there are diverse solutions to perform a given task …
Learning and generalizing cooperative manipulation skills using parametric dynamic movement primitives
This paper presents an approach that generates the overall trajectory of mobile
manipulators for a complex mission consisting of several sub-tasks. Parametric dynamic …
manipulators for a complex mission consisting of several sub-tasks. Parametric dynamic …
A task-learning strategy for robotic assembly tasks from human demonstrations
G Ding, Y Liu, X Zang, X Zhang, G Liu, J Zhao - Sensors, 2020 - mdpi.com
In manufacturing, traditional task pre-programming methods limit the efficiency of human–
robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to …
robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to …
[PDF][PDF] Diffeomorphic Transforms for Generalised Imitation Learning.
We address the generalised imitation learning problem of producing robot motions to imitate
expert demonstrations, while adapting to novel environments. Past studies have often …
expert demonstrations, while adapting to novel environments. Past studies have often …
Geometric fabrics for the acceleration-based design of robotic motion
This paper describes the pragmatic design and construction of geometric fabrics for shaping
a robot's task-independent nominal behavior, capturing behavioral components such as …
a robot's task-independent nominal behavior, capturing behavioral components such as …