[Paper Review] Robotic Self-Assessment of Competence

Gertjan J. Burghouts, A.G. Huizing+1 more|arXiv (Cornell University)|May 4, 2020

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TL;DR

This paper proposes a robotic self-assessment framework that enables AI agents to online evaluate their competence in dynamic environments by detecting unfamiliar situations and requesting human feedback when needed. It uses two methods—zero-shot competence detection and prior-knowledge-based generalization—demonstrating improved reliability in real-world navigation across competent and incompetent scenarios through human-in-the-loop feedback loops.

ABSTRACT

In robotics, one of the main challenges is that the on-board Artificial Intelligence (AI) must deal with different or unexpected environments. Such AI agents may be incompetent there, while the underlying model itself may not be aware of this (e.g., deep learning models are often overly confident). This paper proposes two methods for the online assessment of the competence of the AI model, respectively for situations when nothing is known about competence beforehand, and when there is prior knowledge about competence (in semantic form). The proposed method assesses whether the current environment is known. If not, it asks a human for feedback about its competence. If it knows the environment, it assesses its competence by generalizing from earlier experience. Results on real data show the merit of competence assessment for a robot moving through various environments in which it sometimes is competent and at other times it is not competent. We discuss the role of the human in robot's self-assessment of its competence, and the challenges to acquire complementary information from the human that reinforces the assessments.

Motivation & Objective

• To address the challenge of robotic AI agents being overconfident in unfamiliar or unexpected environments.
• To enable robots to detect when they are operating outside their known competence domain.
• To develop a method for online, real-time assessment of AI competence without prior knowledge or with semantic prior knowledge.
• To integrate human feedback effectively to improve the robot's self-assessment accuracy and reliability.
• To validate the framework in real-world robotic navigation tasks with varying levels of environmental competence.

Proposed method

• The robot uses a novelty detection mechanism to assess whether the current environment is outside its known experience.
• When novelty is detected, the system requests human feedback to calibrate its competence level for the new environment.
• For environments with prior semantic knowledge, the system generalizes from past experiences using a learned competence model.
• The competence model is updated incrementally using feedback, enabling adaptive self-assessment over time.
• The framework integrates human-in-the-loop feedback to refine confidence estimates and reduce overconfidence.
• The system employs a dual-path approach: one for zero-shot competence detection and another for knowledge-enhanced competence estimation.

Experimental results

Research questions

• RQ1 How can a robot detect when it is operating in an unfamiliar environment where its AI model may be incompetent?
• RQ2 What mechanisms enable reliable self-assessment of competence without prior knowledge of the environment?
• RQ3 How can prior semantic knowledge about environments be leveraged to improve competence estimation?
• RQ4 What role does human feedback play in calibrating the robot’s self-assessment of competence?
• RQ5 How does the integration of human feedback improve the robustness and reliability of robotic decision-making in dynamic settings?

Key findings

• The proposed method successfully detected unfamiliar environments in real-world robotic navigation tasks, triggering human feedback when needed.
• Robots using the prior-knowledge-based method generalized competence assessments more accurately than zero-shot detection alone.
• Human feedback significantly improved the calibration of the robot’s confidence, reducing overconfidence in uncertain situations.
• The system demonstrated improved reliability in mixed environments where competence levels varied across locations.
• The framework was validated in real-world settings, showing practical feasibility and effectiveness in dynamic, unpredictable environments.
• The integration of human feedback led to measurable improvements in the robot’s ability to distinguish between competent and incompetent operation.