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Found 16 papers, 3 papers with code
Preference-Conditioned Language-Guided Abstraction
We observe that how humans behave reveals how they see the world.
Getting aligned on representational alignment
Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields.
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation
Policies often fail due to distribution shift -- changes in the state and reward that occur when a policy is deployed in new environments.
Aligning Robot and Human Representations
To act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or mug orientation in its behavior.
SIRL: Similarity-based Implicit Representation Learning
This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not.
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking
One of the most successful paradigms for reward learning uses human feedback in the form of comparisons.
Aligning Robot Representations with Humans
As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging.
Teaching Robots to Span the Space of Functional Expressive Motion
As a result 1) all user feedback can contribute to learning about every emotion; 2) the robot can generate trajectories for any emotion in the space instead of only a few predefined ones; and 3) the robot can respond emotively to user-generated natural language by mapping it to a target VAD.
Inducing Structure in Reward Learning by Learning Features
To get around this issue, recent deep Inverse Reinforcement Learning (IRL) methods learn rewards directly from the raw state but this is challenging because the robot has to implicitly learn the features that are important and how to combine them, simultaneously.
Learning Perceptual Concepts by Bootstrapping from Human Queries
Second, we treat this low-dimensional concept as an automatic labeler to synthesize a large-scale high-dimensional data set with the simulator.
Situational Confidence Assistance for Lifelong Shared Autonomy
Shared autonomy enables robots to infer user intent and assist in accomplishing it.
Dynamically Switching Human Prediction Models for Efficient Planning
As environments involving both robots and humans become increasingly common, so does the need to account for people during planning.
Feature Expansive Reward Learning: Rethinking Human Input
When the correction cannot be explained by these features, recent work in deep Inverse Reinforcement Learning (IRL) suggests that the robot could ask for task demonstrations and recover a reward defined over the raw state space.
Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections
Recent work focuses on how robots can use such input - like demonstrations or corrections - to learn intended objectives.
LESS is More: Rethinking Probabilistic Models of Human Behavior
A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward.
Learning under Misspecified Objective Spaces
Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human's desired objective lies within the robot's hypothesis space.
