Search Results for author:
Found 24 papers, 11 papers with code
Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
A robot operating in a household environment will see a wide range of unique and unfamiliar objects.
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields
This formalism is implemented in three steps: assigning a consistent local coordinate frame to the task-relevant object parts, determining the location and orientation of this coordinate frame on unseen object instances, and executing an action that brings these frames into the desired alignment.
Learning Neuro-Symbolic Skills for Bilevel Planning
Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback.
PG3: Policy-Guided Planning for Generalized Policy Generation
In this work, we study generalized policy search-based methods with a focus on the score function used to guide the search over policies.
Predicate Invention for Bilevel Planning
Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons.
Learning Symbolic Operators for Task and Motion Planning
We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system.
Learning Object-Based State Estimators for Household Robots
The robot may be called upon later to retrieve objects and will need a long-term object-based memory in order to know how to find them.
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
Adding auxiliary losses to the main objective function is a general way of encoding biases that can help networks learn better representations.
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances.
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent.
Meta-learning curiosity algorithms
We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent's life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime.
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards.
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Such models, however, are approximate, which limits their applicability.
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems.
Graph Element Networks: adaptive, structured computation and memory
We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure.
Modular meta-learning in abstract graph networks for combinatorial generalization
Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways.
Learning to guide task and motion planning using score-space representation
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems.
Finding Frequent Entities in Continuous Data
In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of detections.
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
For such complex planning problems, unguided uniform sampling of actions until a path to a goal is found is hopelessly inefficient, and gradient-based approaches often fall short when the optimization manifold of a given problem is not smooth.
Learning to Rank for Synthesizing Planning Heuristics
We investigate learning heuristics for domain-specific planning.
Backward-Forward Search for Manipulation Planning
In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects.
Solving the multiple instance problem with axis-parallel rectangles
The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object.
