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Found 11 papers, 2 papers with code
Practice Makes Perfect: Planning to Learn Skill Parameter Policies
We consider a setting where a robot is initially equipped with (1) a library of parameterized skills, (2) an AI planner for sequencing together the skills given a goal, and (3) a very general prior distribution for selecting skill parameters.
E(2)-Equivariant Graph Planning for Navigation
Learning for robot navigation presents a critical and challenging task.
Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws. These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group.
Equivariant Single View Pose Prediction Via Induced and Restricted Representations
We show that an algorithm that learns a three-dimensional representation of the world from two dimensional images must satisfy certain geometric consistency properties which we formulate as SO(2)-equivariance constraints.
Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation
To alleviate this issue, we propose to differentiate through the Bellman fixed-point equation to decouple forward and backward passes for Value Iteration Network and its variants, which enables constant backward cost (in planning horizon) and flexible forward budget and helps scale up to large tasks.
Integrating Symmetry into Differentiable Planning with Steerable Convolutions
Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry.
Toward Compositional Generalization in Object-Oriented World Modeling
Compositional generalization is a critical ability in learning and decision-making.
Learning Symmetric Embeddings for Equivariant World Models
Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations.
Learning Symmetric Representations for Equivariant World Models
In this paper, we use equivariant transition models as an inductive bias to learn symmetric latent representations in a self-supervised manner.
Model-based Navigation in Environments with Novel Layouts Using Abstract $2$-D Maps
To learn this ability, we need to efficiently train an agent on environments with a small proportion of training maps and share knowledge effectively across the environments.
Deep Imitation Learning for Bimanual Robotic Manipulation
Compared to baselines, our model generalizes better and achieves higher success rates on several simulated bimanual robotic manipulation tasks.
