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Se (3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …
robot manipulation task requires a joint consideration of grasp pose configurations …
Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview
Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …
significant promise for capturing expert motor skills through efficient imitation, facilitating …
Prodmp: A unified perspective on dynamic and probabilistic movement primitives
Movement Primitives (MPs) are a well-known concept to represent and generate modular
trajectories. MPs can be broadly categorized into two types:(a) dynamics-based approaches …
trajectories. MPs can be broadly categorized into two types:(a) dynamics-based approaches …
Deep generative models in robotics: A survey on learning from multimodal demonstrations
Learning from Demonstrations, the field that proposes to learn robot behavior models from
data, is gaining popularity with the emergence of deep generative models. Although the …
data, is gaining popularity with the emergence of deep generative models. Although the …
Diffusion co-policy for synergistic human-robot collaborative tasks
Modeling multimodal human behavior has been a key barrier to increasing the level of
interaction between human and robot, particularly for collaborative tasks. Our key insight is …
interaction between human and robot, particularly for collaborative tasks. Our key insight is …
Stable motion primitives via imitation and contrastive learning
Learning from humans allows nonexperts to program robots with ease, lowering the
resources required to build complex robotic solutions. Nevertheless, such data-driven …
resources required to build complex robotic solutions. Nevertheless, such data-driven …
[HTML][HTML] Continual learning from demonstration of robotics skills
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 …
Robots capable of learning from demonstration can considerably benefit from the added …
[HTML][HTML] Learning stable robotic skills on Riemannian manifolds
In this paper, we propose an approach to learn stable dynamical systems that evolve on
Riemannian manifolds. Our approach leverages a data-efficient procedure to learn a …
Riemannian manifolds. Our approach leverages a data-efficient procedure to learn a …
Riemannian flow matching policy for robot motion learning
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and
synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference …
synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference …
Generative modeling of residuals for real-time risk-sensitive safety with discrete-time control barrier functions
A key source of brittleness for robotic systems is the presence of model uncertainty and
external disturbances. Most existing approaches to robust control either seek to bound the …
external disturbances. Most existing approaches to robust control either seek to bound the …