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Found 14 papers, 0 papers with code
Adaptive Language-Guided Abstraction from Contrastive Explanations
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations.
The Future of Open Human Feedback
In this work, we bring together interdisciplinary experts to assess the opportunities and challenges to realizing an open ecosystem of human feedback for AI.
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input
We derive an approach for learning from these feature-level preferences, both for cases where users specify which features are reward-relevant, and when users do not.
Learning with Language-Guided State Abstractions
We describe a framework for using natural language to design state abstractions for imitation learning.
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.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
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.
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.
Strengthening Subcommunities: Towards Sustainable Growth in AI Research
AI's rapid growth has been felt acutely by scholarly venues, leading to growing pains within the peer review process.
Investigations of Performance and Bias in Human-AI Teamwork in Hiring
In AI-assisted decision-making, effective hybrid (human-AI) teamwork is not solely dependent on AI performance alone, but also on its impact on human decision-making.
What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring
Although systematic biases in decision-making are widely documented, the ways in which they emerge from different sources is less understood.
Human-Machine Collaboration for Fast Land Cover Mapping
This bi-directional feedback loop allows humans to learn how the model responds to new data.
