Simultaneous Forward and Inverse Human-in-the-Loop Optimization

arXiv:2609.22630 · cs.RO · Submitted 2026-09-18 · Read on arXiv

cs.RO

Submitted: 2026-09-18

Updated: 2026-10-12

Comments: Accepted at the 10th Conference on Robot Learning (CoRL 2026)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 16× in this paper · explained in 1 episode
• Human-in-the-Loop — 15× in this paper · explained in 3 episodes
• Reward Function — 9× in this paper · explained in 2 episodes
• Sample Efficiency — 9× in this paper · explained in 4 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Forward modeling — 3× in this paper · explained in 2 episodes
• Pairwise Comparisons — 3× in this paper · explained in 2 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• Active Learning — 2× in this paper · explained in 6 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Simultaneous Forward and Inverse Human-In-the-Loop Optimization — 2× in this paper
• Uncertainty Decomposition — 2× in this paper · explained in 2 episodes
• Acquisition Function — 1× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 1× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Preference-based Learning of Reward Function Features
• Learning Reward Functions by Integrating Human Demonstrations and Preferences
• Active Reward Learning from Online Preferences
• Bayesian Active Learning for Classification and Preference Learning
• RewardBench 2: Advancing Reward Model Evaluation

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