Natural Language Environment (NLE)

• Natural Language Environment is defined as a dynamic interaction space where humans and heterogeneous robots coordinate via natural language to achieve joint tasks.
• The NLE design taxonomy encompasses task coordination dominance, various levels of robot autonomy, and adaptive personality traits to optimize collaborative interactions.
• Empirical VR studies demonstrate that adaptive robot-led coordination boosts task completion rates and user trust in complex, multi-agent environments.

A Natural Language Environment (NLE) is defined as a conceptual interaction space in which humans and multiple heterogeneous robots coordinate primarily through natural language modalities (Liu et al., 19 Jan 2026). The aim of an NLE is to enable seamless human–multi-robot collaboration, leveraging bidirectional language understanding and generation across all agents. NLEs presuppose robots with scene understanding comparable to human perception and the ability to engage in rich intra- and inter-robot dialogue, with differing morphology, autonomy, and personality. This article details the formal structure, design taxonomy, experimental methodologies, empirically-driven refinements, and open challenges associated with NLEs.

1. Formal Definition and Core Characteristics

An NLE is represented as a tuple: $\mathrm{NLE} = \bigl(H,\; R,\; L,\; C \bigr)$ where $H$ is the set of human agents, $R$ denotes robots with diverse capabilities, $L$ is the natural language channel, and $C$ is the set of collaborative tasks.

Key attributes:

• Human–Multi-Robot Collaboration: No single agent can achieve all goals independently; cooperative action is required.
• Human-Level Perception: Robots are considered to possess or to approach scene understanding paralleling human capability.
• Human-Level Language: Robots must both comprehend and produce language at a functional human level.
• Robot Heterogeneity: Morphology, autonomy, and personality differ among robots, resulting in variable interactions and coordination strategies.

2. Taxonomy of Design Dimensions

The preliminary design space for NLEs—synthesized from human–robot interaction literature—comprises three key dimensions, enumerated in Table 1.

Dimension	Categories
Dominance of Task Coordination	Human-dominant ($D_h > \frac{2}{3}$ of events), Robot-dominant ($D_r > \frac{2}{3}$), Adaptive (neither exceeds $2/3$)
Level of Autonomy	Teleoperation, Semi-autonomy (physical/cognitive), Full autonomy
Personality	Anthropomorphism (appearance/voice), Sociability (social behaviors, turn-taking, rapport)

Dominance of Task Coordination measures whether humans, robots, or both adaptively lead key collaboration events. Level of Autonomy spans teleoperation (robot as executor), semi-autonomy with physical or cognitive agency, to full autonomy. Personality includes anthropomorphic cues and sociable conversational behaviors.

3. Virtual Reality Role-Playing Methodology

To empirically examine NLE interactions, a role-playing study was conducted using immersive VR (Unity, Meta Quest Pro). Each session included four roles: one human, a cleaning robot, and two assistive robots specializing in mobile manipulation.

Experimental Protocol:

• Sessions: 13, each with 4 participants (total 52).
• Tasks: Six sub-tasks in a five-room apartment (e.g., dispose trash, vacuum floor, organize toys, etc.).
• Robot Interface: Simulated SLAM maps, object detection overlays, affordance cues, and to-do lists mimicking LLM-driven output.
• Human Interface: No overlays; maximal mobility and bimodal manipulation functions.
• Metrics Collected:
  • Likert scales for perceived immersion and interaction ease.
  • Task and sub-task completion rates.
  • Task-coordination event frequencies and leadership statistics.
  • Autonomy levels categorization.
  • Semi-structured interviews and language data logs.

4. Empirical Refinement of NLE Taxonomy

Quantitative and qualitative data analyses led to substantive refinements in the original design space.

Findings:

• Collaboration Patterns: Half of all 78 sub-tasks required joint human–multi-robot action.
• Task Coordination Dominance: Out of 13 sessions—robot-dominant (5), adaptive (5), human-dominant (3). 100% task completion was more strongly associated with robot-dominant/adaptive sessions.
• Autonomy Breakdown:
  • Routine tasks (e.g., vacuuming): full autonomy utilized in ~80% of cases.
  • Complex tasks (e.g., tableware sorting): shift toward semi-autonomy.
  • Collaborative tasks (e.g., multi-step cleaning): teleoperation follower dynamics observed.
• Thematic Interview Coding:
  • Trust affected by task success, transparency, and robot responsiveness.
  • Divergent preferences for dominance and autonomy contingent on routine vs. novel task types.
  • Desire for robot personalities that can be distinct yet consistent and context-adaptive.

5. Finalized Design Space and Its Subcategories

Study insights prompted a refined design space:

5.1 Task Coordination Dominance

• Human-dominant
• Robot-dominant
• Adaptive
  • Contextual nuance: dominance hierarchy may be split between high-level (strategic) and low-level (tactical) activities.

5.2 Robot Autonomy

Defined across four levels: $$A \in \{\text{Teleop},\,\text{Semi\_Phys},\,\text{Semi\_Cog},\,\text{Full}\}$$

• Teleop: Execution-only.
• Semi_Phys: Physical actions; queries human for context.
• Semi_Cog: Autonomous reasoning; seeks human help for physical task execution.
• Full: Unconstrained autonomy.

5.3 Robot Personality

• Anthropomorphism (appearance/voice)
• Sociability (social behaviors)
• Consistency vs. Customization (user preference favors neutral defaults with opt-in personalization)
• Adaptivity (real-time responsiveness to user sentiment cues)

6. Design Implications and Emergent Tensions

The empirical study reveals several tensions and actionable implications for future NLE deployment:

• Autonomy vs. Trust/Privacy: Elevated autonomy improves task efficiency but can erode user trust or privacy sensitivity. Gradual autonomy escalation combined with teleop corrections and privacy-preserving offline LLMs is recommended.
• Dominance Balancing: Although users prefer human leadership, many accept robot-led collaboration given demonstrated robot competence. Transparent robot explanations and hybrid delegation structures (e.g., "master robot") are beneficial.
• Personality Adaptation: Sociability can foster engagement but also fatigue those seeking purely utilitarian interaction. Dynamic adjustment based on sentiment analytics mitigates potential user exhaustion.
• Inter-Robot Communication Transparency: Excessive robot-robot audible dialogue may be perceived as distracting or unsettling. Background coordination with selective speech disclosure enhances user comfort.

7. Prospective Directions and Open Challenges

Future research priorities and challenges include:

• Ecological Validation: In-home trials addressing variances in physical environments and dynamic emotional contexts.
• Expanding Robot Typological Diversity: Inclusion of robots specialized for cooking, security, and healthcare to test taxonomy generalizability.
• Task-Specific Demands: Exploration of domains with acute autonomy and explainability needs (e.g., technical repair, medical assistance).
• Accessibility for Vulnerable Populations: Tailoring NLEs for elderly and disabled users to ensure optimal autonomy, privacy, and usability trade-offs.
• Longitudinal Adaptation: Analyzing the evolution of dominance, autonomy, and personality preferences over extended interaction durations.

Natural Language Environments, as conceptualized in this foundational study, present an empirically validated design space structured around task coordination dominance, robot autonomy, and personality—each interwoven with concrete tensions and strategic opportunities for robust multi-agent collaboration (Liu et al., 19 Jan 2026).