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Found 22 papers, 7 papers with code
Vision and Language Navigation in the Real World via Online Visual Language Mapping
Directly transferring SOTA navigation policies trained in simulation to the real world is challenging due to the visual domain gap and the absence of prior knowledge about unseen environments.
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.
One-shot Imitation Learning via Interaction Warping
We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration.
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture.
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.
Robust Imitation of a Few Demonstrations with a Backwards Model
On continuous control domains, we evaluate the robustness when starting from different initial states unseen in the demonstration data.
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.
Binding Actions to Objects in World Models
We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms.
Factored World Models for Zero-Shot Generalization in Robotic Manipulation
In this paper, we learn to generalize over robotic pick-and-place tasks using object-factored world models, which combat the combinatorial explosion by ensuring that predictions are equivariant to permutations of objects.
Natural Language for Human-Robot Collaboration: Problems Beyond Language Grounding
To enable robots to instruct humans in collaborations, we identify several aspects of language processing that are not commonly studied in this context.
Bad-Policy Density: A Measure of Reinforcement Learning Hardness
We address this question by proposing a simple measure of reinforcement-learning hardness called the bad-policy density.
Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps
In this work, we propose an approach that leverages a rough 2-D map of the environment to navigate in novel environments without requiring further learning.
Action Priors for Large Action Spaces in Robotics
This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks.
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.
Learning Discrete State Abstractions With Deep Variational Inference
In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction.
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands.
Modeling Latent Attention Within Neural Networks
Deep neural networks are able to solve tasks across a variety of domains and modalities of data.
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular.
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
We refer to this attribute-based representation as a world model, and consider how to acquire it via noisy perception and maintain it over time, as objects are added, changed, and removed in the world.
