Robotic Phenomenology: Grammars of Behavior
M. Pomarlan, Amber Maimon, Shiyao Zhang, R. Porzel, Rainer Malaka, Iddo Yehoshua Wald
CHI Extended Abstracts April 13, 2026 DOI: 10.1145/3772363.3798836 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Topics | Philosophy of mind |
Abstract
Embodied cognition holds that behavior is shaped by the continuous coupling between internal bodily regulation and engagement with the environment. Robots likewise act under dynamically changing internal conditions; yet, such variables are typically hidden implementation details rather than seen as contributors to behavioral organization. We introduce a framework for robotic phenomenology to externalize how internal variables participate in shaping behavioral structure over time. Interoceptive and exteroceptive signals are jointly modeled as symbolic sequences, from which grammar-based representations of behavior are induced. These representations allow individual activity episodes to be summarized as structured motifs and interpreted using an LLM as first-person, phenomenology-like behavioral reports. We demonstrate the approach on an obstacle avoidance task, incorporating battery voltage change alongside environmental sensing. Rather than claiming that robots have individual experience, this work offers a tool for analyzing internal–external coupling as an organizational feature of embodied behavior.
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