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Learning When to Ask for Help: Efficient Interactive Navigation via Implicit Uncertainty Estimation

Ifueko Igbinedion, Sertac Karaman

TL;DR

This work tackles robust autonomous navigation in unseen environments by introducing a semi-autonomous framework that learns when to request human input through an interaction policy. The interaction decision is guided by an implicit uncertainty signal derived from the autonomous policy's feature extraction, with certainty defined as $c_t = |P(a_h| heta_t) - P(a_r| heta_t)|$ and uncertainty as $(1 - c_t)$. A pretrained point-navigation policy serves as the base, and an auxiliary interaction policy is trained with PPO to optimize a joint reward, simulating a human expert when necessary. Across Habitat HM3D and FlightGoggles, the approach yields notable gains in success (e.g., a 0.38 improvement) at modest human interaction rates (e.g., 0.3), and demonstrates zero-shot transfer to a new environment achieving 0.92 success with 0.23 interaction rate using a real human expert. This method offers a practical route for robots to learn from humans with limited real-time input, enhancing performance in real-world settings without full teleoperation.

Abstract

Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, data collection and model refinement may be impractical in every environment. Approaches that utilize human demonstrations through manual operation can aid in refinement and generalization, but often require significant data collection efforts to generate enough demonstration data to achieve satisfactory task performance. Interactive approaches allow for humans to provide correction to robot action in real time, but intervention policies are often based on explicit factors related to state and task understanding that may be difficult to generalize. Addressing these challenges, we train a lightweight interaction policy that allows robots to decide when to proceed autonomously or request expert assistance at estimated times of uncertainty. An implicit estimate of uncertainty is learned via evaluating the feature extraction capabilities of the robot's visual navigation policy. By incorporating part-time human interaction, robots recover quickly from their mistakes, significantly improving the odds of task completion. Incorporating part-time interaction yields an increase in success of 0.38 with only a 0.3 expert interaction rate within the Habitat simulation environment using a simulated human expert. We further show success transferring this approach to a new domain with a real human expert, improving success from less than 0.1 with an autonomous agent to 0.92 with a 0.23 human interaction rate. This approach provides a practical means for robots to interact and learn from humans in real-world settings.

Learning When to Ask for Help: Efficient Interactive Navigation via Implicit Uncertainty Estimation

TL;DR

This work tackles robust autonomous navigation in unseen environments by introducing a semi-autonomous framework that learns when to request human input through an interaction policy. The interaction decision is guided by an implicit uncertainty signal derived from the autonomous policy's feature extraction, with certainty defined as and uncertainty as . A pretrained point-navigation policy serves as the base, and an auxiliary interaction policy is trained with PPO to optimize a joint reward, simulating a human expert when necessary. Across Habitat HM3D and FlightGoggles, the approach yields notable gains in success (e.g., a 0.38 improvement) at modest human interaction rates (e.g., 0.3), and demonstrates zero-shot transfer to a new environment achieving 0.92 success with 0.23 interaction rate using a real human expert. This method offers a practical route for robots to learn from humans with limited real-time input, enhancing performance in real-world settings without full teleoperation.

Abstract

Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, data collection and model refinement may be impractical in every environment. Approaches that utilize human demonstrations through manual operation can aid in refinement and generalization, but often require significant data collection efforts to generate enough demonstration data to achieve satisfactory task performance. Interactive approaches allow for humans to provide correction to robot action in real time, but intervention policies are often based on explicit factors related to state and task understanding that may be difficult to generalize. Addressing these challenges, we train a lightweight interaction policy that allows robots to decide when to proceed autonomously or request expert assistance at estimated times of uncertainty. An implicit estimate of uncertainty is learned via evaluating the feature extraction capabilities of the robot's visual navigation policy. By incorporating part-time human interaction, robots recover quickly from their mistakes, significantly improving the odds of task completion. Incorporating part-time interaction yields an increase in success of 0.38 with only a 0.3 expert interaction rate within the Habitat simulation environment using a simulated human expert. We further show success transferring this approach to a new domain with a real human expert, improving success from less than 0.1 with an autonomous agent to 0.92 with a 0.23 human interaction rate. This approach provides a practical means for robots to interact and learn from humans in real-world settings.
Paper Structure (17 sections, 4 equations, 6 figures, 2 tables)

This paper contains 17 sections, 4 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Human-in-the-loop Robotic Planning
  4. Offline Imitation Learning
  5. Interactive Imitation Learning
  6. Approach
  7. Navigation Policy Design and Optimization
  8. Autonomous Policy
  9. Interaction Policy
  10. Simulation Environments
  11. Human Interaction Design
  12. Results
  13. Semi-Autonomous Navigation
  14. Zero-Shot Transfer with a Real Human Expert
  15. Future Work
  16. ...and 2 more sections

Figures (6)

  • Figure 1: The impact of part-time intervention on autonomous agent execution. Points of semi-autonomous control are shown in blue, while autonomous execution is shown in red. When agents are empowered with the ability to decide when to ask for help at points of uncertainty, navigating difficult areas within new environments becomes easy.
  • Figure 2: Semi-autonomous system design. The simulation environment generates observations that are given to the autonomous agent, which communicates with a help policy that interacts with the human expert to return a set of optimal actions. The agent then directly interacts with the environment using its own predictions and manual commands from part-time human interaction.
  • Figure 3: Human interaction interface design. Human experts are presented with the same information as autonomous agents, but presented in a human readable format. Humans are not given top-level maps during navigation.
  • Figure 4: Examples of the impact of uncertainty estimation in the selection of optimal points of expert intervention. Pairs of interactions are side by side, with autonomous execution on the right. Rather than getting stuck within dead ends, loops, or tight sets of obstacles, expert intervention avoids these areas of catastrophic failure.
  • Figure 5: Human intervention reducing wasteful exploratory behavior. When agents struggle to progress towards the goal location, exploration is sometimes effective, but may become wasteful in more complex scenes. Relying on expert interaction significantly reduces this waste.
  • ...and 1 more figures