
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards
Reinforcement learning (RL), particularly in sparse reward settings, oft...
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Bayesian Robust Optimization for Imitation Learning
One of the main challenges in imitation learning is determining what act...
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Efficiently Guiding Imitation Learning Algorithms with Human Gaze
Human gaze is known to be an intentionrevealing signal in human demonst...
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Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Bayesian reward learning from demonstrations enables rigorous safety and...
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Local Nonparametric MetaLearning
A central goal of metalearning is to find a learning rule that enables ...
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Deep Bayesian Reward Learning from Preferences
Bayesian inverse reinforcement learning (IRL) methods are ideal for safe...
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Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty
Sudden changes in the dynamics of robotic tasks, such as contact with an...
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Understanding Teacher Gaze Patterns for Robot Learning
Human gaze is known to be a strong indicator of underlying human intenti...
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RankingBased Reward Extrapolation without Rankings
The performance of imitation learning is typically upperbounded by the ...
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A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
A key challenge in intelligent robotics is creating robots that are capa...
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HypothesisDriven Skill Discovery for Hierarchical Deep Reinforcement Learning
Deep reinforcement learning encompasses many versatile tools for designi...
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UncertaintyAware Data Aggregation for Deep Imitation Learning
Estimating statistical uncertainties allows autonomous agents to communi...
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Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
A critical flaw of existing inverse reinforcement learning (IRL) methods...
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Using Natural Language for Reward Shaping in Reinforcement Learning
Recent reinforcement learning (RL) approaches have shown strong performa...
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RiskAware Active Inverse Reinforcement Learning
Active learning from demonstration allows a robot to query a human for s...
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LAAIR: A Layered Architecture for Autonomous Interactive Robots
When developing general purpose robots, the overarching software archite...
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Towards Online Learning from Corrective Demonstrations
Robots operating in realworld human environments will likely encounter ...
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Learning MultiStep Robotic Tasks from Observation
Due to burdensome data requirements, learning from demonstration often f...
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Importance Sampling Policy Evaluation with an Estimated Behavior Policy
In reinforcement learning, offpolicy evaluation is the task of using da...
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Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
Inverse reinforcement learning (IRL) infers a reward function from demon...
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Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Noisy observations coupled with nonlinear dynamics pose one of the bigge...
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Leveraging Task Knowledge for Robot Motion Planning Under Uncertainty
Noisy observations coupled with nonlinear dynamics pose one of the bigge...
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Safe Reinforcement Learning via Shielding
Reinforcement learning algorithms discover policies that maximize reward...
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Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
In the field of reinforcement learning there has been recent progress to...
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DataEfficient Policy Evaluation Through Behavior Policy Search
We consider the task of evaluating a policy for a Markov decision proces...
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Bootstrapping with Models: Confidence Intervals for OffPolicy Evaluation
For an autonomous agent, executing a poor policy may be costly or even d...
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Scott Niekum
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