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Integrated Task and Motion Planning
The problem of planning for a robot that operates in environments contai...
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Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
From CNNs to attention mechanisms, encoding inductive biases into neural...
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Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Real-world planning problems often involve hundreds or even thousands of...
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CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
Meta-planning, or learning to guide planning from experience, is a promi...
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Learning compositional models of robot skills for task and motion planning
The objective of this work is to augment the basic abilities of a robot ...
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Visual Prediction of Priors for Articulated Object Interaction
Exploration in novel settings can be challenging without prior experienc...
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Meta-learning curiosity algorithms
We hypothesize that curiosity is a mechanism found by evolution that enc...
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GLIB: Exploration via Goal-Literal Babbling for Lifted Operator Learning
We address the problem of efficient exploration for learning lifted oper...
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Online Replanning in Belief Space for Partially Observable Task and Motion Problems
To solve multi-step manipulation tasks in the real world, an autonomous ...
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Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a comp...
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Graph Element Networks: adaptive, structured computation and memory
We explore the use of graph neural networks (GNNs) to model spatial proc...
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Few-Shot Bayesian Imitation Learning with Logic over Programs
We describe an expressive class of policies that can be efficiently lear...
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Every Local Minimum is a Global Minimum of an Induced Model
For non-convex optimization in machine learning, this paper proves that ...
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Look before you sweep: Visibility-aware motion planning
This paper addresses the problem of planning for a robot with a directio...
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Elimination of All Bad Local Minima in Deep Learning
In this paper, we theoretically prove that we can eliminate all suboptim...
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Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Bayesian optimization usually assumes that a Bayesian prior is given. Ho...
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Effect of Depth and Width on Local Minima in Deep Learning
In this paper, we analyze the effects of depth and width on the quality ...
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Learning sparse relational transition models
We present a representation for describing transition models in complex ...
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Learning Quickly to Plan Quickly Using Modular Meta-Learning
Multi-object manipulation problems in continuous state and action spaces...
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Learning to guide task and motion planning using score-space representation
In this paper, we propose a learning algorithm that speeds up the search...
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Planning to Give Information in Partially Observed Domains with a Learned Weighted Entropy Model
In many real-world robotic applications, an autonomous agent must act wi...
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Active model learning and diverse action sampling for task and motion planning
The objective of this work is to augment the basic abilities of a robot ...
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Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization
In partially observed environments, it can be useful for a human to prov...
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STRIPStream: Integrating Symbolic Planners and Blackbox Samplers
Many planning applications involve complex relationships defined on high...
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Sampling-Based Methods for Factored Task and Motion Planning
This paper presents a general-purpose formulation of a large class of di...
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Learning to select examples for program synthesis
Program synthesis is a class of regression problems where one seeks a so...
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Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
In robotics, it is essential to be able to plan efficiently in high-dime...
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Generalization in Deep Learning
This paper explains why deep learning can generalize well, despite large...
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STRIPS Planning in Infinite Domains
Many robotic planning applications involve continuous actions with highl...
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Learning to Rank for Synthesizing Planning Heuristics
We investigate learning heuristics for domain-specific planning. Prior w...
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Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
We introduce a framework for model learning and planning in stochastic d...
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Backward-Forward Search for Manipulation Planning
In this paper we address planning problems in high-dimensional hybrid co...
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Bayesian Optimization with Exponential Convergence
This paper presents a Bayesian optimization method with exponential conv...
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Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
To accomplish tasks in human-centric indoor environments, robots need to...
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Learning to Cooperate via Policy Search
Cooperative games are those in which both agents share the same payoff s...
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Deliberation Scheduling for Time-Critical Sequential Decision Making
We describe a method for time-critical decision making involving sequent...
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Hierarchical Solution of Markov Decision Processes using Macro-actions
We investigate the use of temporally abstract actions, or macro-actions,...
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Accelerating EM: An Empirical Study
Many applications require that we learn the parameters of a model from d...
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Learning Finite-State Controllers for Partially Observable Environments
Reactive (memoryless) policies are sufficient in completely observable M...
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Solving POMDPs by Searching the Space of Finite Policies
Solving partially observable Markov decision processes (POMDPs) is highl...
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Adaptive Importance Sampling for Estimation in Structured Domains
Sampling is an important tool for estimating large, complex sums and int...
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The Thing That We Tried Didn't Work Very Well : Deictic Representation in Reinforcement Learning
Most reinforcement learning methods operate on propositional representat...
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