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[2602.18603] Enhancing Goal Inference via Correction Timing
Summary
This article explores how the timing of human corrections can enhance robot learning by providing insights into task objectives and improving goal inference.
Why It Matters
Understanding how correction timing influences robot learning is crucial for developing more effective human-robot interactions. This research could lead to advancements in robotics, making robots more intuitive and responsive to human feedback, which is essential for applications in various fields such as automation, healthcare, and service industries.
Key Takeaways
- Correction timing can serve as a valuable signal for robot learning.
- The study identifies features of robot motion that prompt human corrections.
- Timing and direction of corrections can help infer the final goal of human feedback.
- Improved learning outcomes were observed in applications related to goal inference.
- This research highlights the importance of human feedback in robotic task performance.
Computer Science > Robotics arXiv:2602.18603 (cs) [Submitted on 20 Feb 2026] Title:Enhancing Goal Inference via Correction Timing Authors:Anjiabei Wang, Shuangge Wang, Tesca Fitzgerald View a PDF of the paper titled Enhancing Goal Inference via Correction Timing, by Anjiabei Wang and 1 other authors View PDF HTML (experimental) Abstract:Corrections offer a natural modality for people to provide feedback to a robot, by (i) intervening in the robot's behavior when they believe the robot is failing (or will fail) the task objectives and (ii) modifying the robot's behavior to successfully fulfill the task. Each correction offers information on what the robot should and should not do, where the corrected behavior is more aligned with task objectives than the original behavior. Most prior work on learning from corrections involves interpreting a correction as a new demonstration (consisting of the modified robot behavior), or a preference (for the modified trajectory compared to the robot's original behavior). However, this overlooks one essential element of the correction feedback, which is the human's decision to intervene in the robot's behavior in the first place. This decision can be influenced by multiple factors including the robot's task progress, alignment with human expectations, dynamics, motion legibility, and optimality. In this work, we investigate whether the timing of this decision can offer a useful signal for inferring these task-relevant influences. In particu...
