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Reducing Human-Robot Goal State Divergence with Environment Design

Kelsey Sikes, Sarah Keren, Sarath Sreedharan

TL;DR

This work introduces Goal State Divergence ($\mathcal{GSD}$) as a quantitative measure of mismatch between a human's expected robot goal state and the robot's actual final state, and formalizes the HRGA design problem to minimize this divergence through environment design. It develops bound-based approximations ($\mathcal{GD}^{\uparrow}$ and $\mathcal{GD}^{\downarrow}$) and a planning-based compilation to compute these bounds efficiently, enabling automatic identification of minimal environment modifications. The authors propose an inner-outer loop algorithm to search for the smallest set of design changes that enforce zero lower-bound divergence while maintaining an acceptable upper bound, and validate the approach on IPC-domain benchmarks, showing favorable performance over naive baselines. The results demonstrate the feasibility and efficiency of environment design for improving human–robot goal alignment and safety, with potential extensions to richer task specifications and dynamic environments.

Abstract

One of the most difficult challenges in creating successful human-AI collaborations is aligning a robot's behavior with a human user's expectations. When this fails to occur, a robot may misinterpret their specified goals, prompting it to perform actions with unanticipated, potentially dangerous side effects. To avoid this, we propose a new metric we call Goal State Divergence $\mathcal{(GSD)}$, which represents the difference between a robot's final goal state and the one a human user expected. In cases where $\mathcal{GSD}$ cannot be directly calculated, we show how it can be approximated using maximal and minimal bounds. We then input the $\mathcal{GSD}$ value into our novel human-robot goal alignment (HRGA) design problem, which identifies a minimal set of environment modifications that can prevent mismatches like this. To show the effectiveness of $\mathcal{GSD}$ for reducing differences between human-robot goal states, we empirically evaluate our approach on several standard benchmarks.

Reducing Human-Robot Goal State Divergence with Environment Design

TL;DR

This work introduces Goal State Divergence () as a quantitative measure of mismatch between a human's expected robot goal state and the robot's actual final state, and formalizes the HRGA design problem to minimize this divergence through environment design. It develops bound-based approximations ( and ) and a planning-based compilation to compute these bounds efficiently, enabling automatic identification of minimal environment modifications. The authors propose an inner-outer loop algorithm to search for the smallest set of design changes that enforce zero lower-bound divergence while maintaining an acceptable upper bound, and validate the approach on IPC-domain benchmarks, showing favorable performance over naive baselines. The results demonstrate the feasibility and efficiency of environment design for improving human–robot goal alignment and safety, with potential extensions to richer task specifications and dynamic environments.

Abstract

One of the most difficult challenges in creating successful human-AI collaborations is aligning a robot's behavior with a human user's expectations. When this fails to occur, a robot may misinterpret their specified goals, prompting it to perform actions with unanticipated, potentially dangerous side effects. To avoid this, we propose a new metric we call Goal State Divergence , which represents the difference between a robot's final goal state and the one a human user expected. In cases where cannot be directly calculated, we show how it can be approximated using maximal and minimal bounds. We then input the value into our novel human-robot goal alignment (HRGA) design problem, which identifies a minimal set of environment modifications that can prevent mismatches like this. To show the effectiveness of for reducing differences between human-robot goal states, we empirically evaluate our approach on several standard benchmarks.
Paper Structure (14 sections, 4 theorems, 7 equations, 1 figure, 1 table, 1 algorithm)

This paper contains 14 sections, 4 theorems, 7 equations, 1 figure, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Running Example
  5. Design to Reduce Goal State Divergence
  6. Calculating $\mathcal{GD}^{\uparrow}$ and $\mathcal{GD}^{\downarrow}$
  7. Remark
  8. Identifying Minimal Designs for HRGA
  9. Inner Loop for Identifying Designs
  10. Evaluation
  11. Dataset
  12. Setup
  13. Results
  14. Conclusion

Key Result

Proposition 1

For the robot and human model pair $\mathcal{M^R}$ and $\mathcal{M^H}$, the maximal goal state divergence is guaranteed to be greater than or equal to the goal state divergence for the human plan $\pi^\mathcal{H}$ and the robot plan $\pi^\mathcal{R}$, i.e., $\mathcal{GD}^{\uparrow}(\mathcal{M^H}, \m

Figures (1)

  • Figure 1: In a greenhouse setting, a human asks a robot to water plants based on their incorrect beliefs about its model. As a result, the robot follows the least costliest plan and chooses to water the plants with a hose, causing a fire. Using environment design, the hose is removed from the scene to avoid potential safety issues.

Theorems & Definitions (11)

  • Definition 1
  • Definition 2
  • Definition 3
  • Proposition 1
  • Definition 4
  • Proposition 2
  • Definition 5
  • Definition 6
  • Proposition 3
  • proof
  • ...and 1 more