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Found 7 papers, 1 papers with code
D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation
In this work, we propose D-Cubed, a novel trajectory optimisation method using a latent diffusion model (LDM) trained from a task-agnostic play dataset to solve dexterous deformable object manipulation tasks.
World Models via Policy-Guided Trajectory Diffusion
Our results demonstrate that PolyGRAD outperforms state-of-the-art baselines in terms of trajectory prediction error for short trajectories, with the exception of autoregressive diffusion.
TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer
The teacher world model then supervises a student world model that takes the domain-randomised image observations as input.
Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space
Motion planning framed as optimisation in structured latent spaces has recently emerged as competitive with traditional methods in terms of planning success while significantly outperforming them in terms of computational speed.
Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments
In this work, we propose a system for efficient skill acquisition that leverages an object-centric generative model (OCGM) for versatile goal identification to specify a goal for MP combined with RL to solve complex manipulation tasks in obstructed environments.
Task-Induced Representation Learning
We investigate the effectiveness of unsupervised and task-induced representation learning approaches on four visually complex environments, from Distracting DMControl to the CARLA driving simulator.
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.
