Robots learn better when asked to clarify confusing instructions
Researchers at HRI 2026 found that robots using interactive questioning to understand human feedback outperform those that blindly follow instructions. The advance matters because it makes deploying robots in real-world settings—warehouses, hospitals, homes—faster and cheaper by reducing the need for expert trainers.
Originaltitel: Clarifying Constraints in Interactive Robot Learning with Language Feedback
<p>Using non-expert language feedback in learning is crucial to making robots successful in human-centered environments. While language feedback has shown potential to teach robots complex tasks, it also brings challenges: humans leave important context, detail, and clarifications unspoken, making interactive approaches necessary to use the feedback effectively. In this work, we develop an interactive robot learning system that can ask clarifying questions to differentiate between hard and soft task constraints from user verbal feedback. The system uses feedback as either shields (hard constraints) or to shape the reward (soft constraints). We conducted a user study with 24 participants, comparing the use of both hard and soft constraints versus two baseline conditions. We show that participants significantly prefer a system using a combination of both hard and soft constraints, or only using soft constraints, compared to a system using only hard constraints. Qualitative analysis of the participants' interactions with the system revealed common feedback types: spatial, temporal and meta-level. To evaluate the learning performance of the system, we conducted simulated experiments showing that combining both hard and soft constraints performs best in terms of reaching high rewards and finding an efficient solution. Additionally, we provide demonstrations of our system on real robot hardware.</p>