Robot Synesthesia in Multisensory Robotics

Updated 20 December 2025

Robot synesthesia is the engineered cross-modal mapping in robotic systems that fuses sensory data to achieve robust perception, control, and expressive output.
It leverages advanced sensor fusion, cross-modal latent embedding, and self-attention architectures to translate and integrate visual, auditory, and tactile signals.
Experimental studies show improved manipulation, artistic creation, and intuitive human-robot communication by operationalizing multisensory integration.

Robot synesthesia refers to the engineered cross-modal mapping or fusion of disparate sensory, perceptual, or control signals within robotic systems, enabling robots to integrate, translate, or “perceive” information across modalities in a way that can serve functional, interpretive, expressive, or communicative goals. The concept draws upon the neurological condition of synesthesia in humans, where stimulation of one sensory or cognitive pathway leads to involuntary experiences in another. In robotics, synesthesia can manifest in direct sensor fusion architectures, cross-modal mappings for interpretability, affective or artistic transformation (e.g., sound to color), and interface strategies for human-robot interaction. Recent research operationalizes robot synesthesia both as multi-sensory representation learning and as cross-modal artistic or communicative output, covering direct sensor fusion, aesthetic translation, and transparent sensor-to-sense mappings for intent legibility.

1. Definitions and Taxonomy

Robot synesthesia encompasses at least three technically distinct paradigms:

Multisensory Fusion: Joint real-time integration of multiple sensor modalities (vision, audition, touch, RF) for perception and control, as in self-attention models fusing visual, acoustic, and tactile signals for manipulation tasks (Li et al., 2022), or point-cloud fusion of visuotactile data for dexterous in-hand manipulation (Yuan et al., 2023).
Cross-Modal Mapping for Expressiveness or Intent: Transformation of abstract internal states, sensor features, or control signals into alternative modalities, supporting artistic creation or transparent intent communication—for example, mapping musical harmony to robot movement and painting (Cheng et al., 31 May 2025), or projecting internal robot variables into immersive sensory metaphors for public understanding (Chen et al., 9 Oct 2025).
Communicative/Perceptual Sonification: Encoding data or robot states into sound via computationalist (parameter-to-audio) or ecological (cognitive-metaphor) strategies, as in spatialized audio cues for operator teams in teleoperation (Simmons et al., 12 Jul 2024) or movement-driven musical outputs in everyday robots (Cuan et al., 2023).

A summary of key forms is presented below:

Paradigm	Exemplars	Primary Research Function
Multisensory Fusion	(Li et al., 2022, Yuan et al., 2023, Cheng et al., 13 Jan 2025)	Perceptual robustness, dexterity
Artistic Mapping	(Cheng et al., 31 May 2025, Misra et al., 2023)	Creative expression, emotion rendering
Intent/State Display	(Chen et al., 9 Oct 2025, Cuan et al., 2023, Simmons et al., 12 Jul 2024)	Interpretability, HRI, safety

2. Architectures and Technical Mechanisms

2.1 Multisensory Fusion Networks

In high-dexterity manipulation, robot synesthesia is realized through input-level fusion of multimodal sensor data. For example, visuotactile robot synesthesia projects tactile contact locations into a shared 3D point cloud with visual depth-camera data, allowing a PointNet encoder to operate over a unified spatial representation (Yuan et al., 2023). In tri-modal fusion, vision, audio, and touch are jointly embedded (via modified ResNet encoders per modality) and fused via multi-head self-attention, yielding a temporally adaptive meta-representation (Li et al., 2022).

Such architectures contrast with late-fusion or hand-tuned heuristics, permitting end-to-end learning of optimal cross-modal saliency for specific tasks (e.g., packing, pouring, in-hand rotation).

Artistic robot synesthesia systems employ differentiable mappings between non-overlapping domains—e.g., audio or speech features and visual art. In swarm painting, musical features such as chord (harmony), tempo, and emotion are algorithmically mapped to painting parameters. Each chord is mapped to an emotion, then to a color (via Plutchik’s wheel), spatial location (chord wheel), and density function, while tempo governs motion responsiveness (stroke agility versus smoothness). The robots’ coordinated movements render a distributed light-painting or pigment release pattern, manifesting a real-time synesthetic translation of music into visual art (Cheng et al., 31 May 2025).

For speech and emotion-driven painting, the robot uses cross-modal latent embeddings: audio and image encoders are aligned, and emotions extracted from speech are passed as conditioning signals steering the stylistic and color properties of the generated painting. Gradient-based optimization in the shared latent space enforces desired content and emotional alignment (Misra et al., 2023). This directly operationalizes the principle of synesthesia for expressive, affect-driven robot art.

2.3 Sonification and Perceptual Display

Machine intent or environmental state can be rendered via real-time sonification. Drawing a distinction between computationalist (parameter-to-sound) and cognitive-metaphor (ecological) mappings, (Simmons et al., 12 Jul 2024) demonstrates that abstract sensor signals (e.g., radiation, temperature, gas density) can be encoded in either intuitive metaphoric soundscapes (Geiger clicks, hissing, throbbing tones) or standardized auditory dimensions (pitch, brightness, noise), with differential impacts on predictability, legibility, and user workload.

Robots can embody their own movements musically by mapping joint velocities and control signals onto instrument-triggered sounds, thereby synchronizing machine behavior with musical output; this has demonstrated measurable impact on human perception of robot intelligence and likability (Cuan et al., 2023).

In installations like Airy, real-time control variables (e.g., fabric height, tension, timing) are algorithmically mapped to projected visual metaphors (rising forests, dynamic weather states) that invert the traditional black-box status of robot controllers, making robot intent viscerally legible to untrained audiences (Chen et al., 9 Oct 2025).

3. Representative Applications

3.1 Manipulation and Perception

Dexterous In-Hand Manipulation: Visuotactile fusion (“robot synesthesia”) allows a robot to jointly process visual and contact cues as a single point cloud, yielding substantially improved generalization and sim-to-real transfer in rotation and insertion tasks relative to unimodal or late-fusion baselines. Quantitatively, a synesthetic fusion model achieves higher cumulative rotation reward (CRR) and longer time-to-fall (TTF) on both simulated and real objects (Yuan et al., 2023).
Tri-Modal Dense Packing and Pouring: A robot combining real-time vision, tactile, and contact-microphone input, fused via self-attention, achieves 100% task success in dense packing and lowest error in precision pouring, outperforming bimodal and concatenation baselines (Li et al., 2022).
Sensing-Communication Co-Design: Multi-modal datasets (e.g., SynthSoM) offer precise alignment across RF, radar, camera, and LiDAR data, supporting sensor–communication fusion architectures for robust localization, segmentation, and channel estimation, crucial for future “synesthetic” 6G robot platforms (Cheng et al., 13 Jan 2025).

3.2 Creative Synesthetic Robotics

Music-to-Painting: Distributed robot swarms convert live musical input into coordinated pigment release (or LED light trails), with feature-to-feature mappings—chord to color/emotion/location, tempo to motion style—forming the backbone of the system’s synesthetic translation. Artistic outputs demonstrate quantitative increases in coverage and visual complexity with larger swarms and mixed pigments, while informal evaluations rate expressiveness highly (Cheng et al., 31 May 2025).
Sound-Emotion-Guided Painting: In the “S-FRIDA” system, audio clip or speech content is embedded in a cross-modal latent space with target images, and emotion labels extracted from the input are mapped into the painting style. User studies show participants can identify the sound or emotion corresponding to a painting significantly better than chance (Misra et al., 2023).

3.3 Human-Robot Interaction and Interpretability

Intent Transparency: Intent and strategy of reinforcement-trained multi-agent systems can be communicated via sensor-to-sense mapping (e.g., transforming robot control variables into projective visuals). Observations confirm that human spectators intuitively recognize strategies and emotional valence in competitive/cooperative scenarios, suggesting robot synesthesia as an interface principle for democratized AI interpretability (Chen et al., 9 Oct 2025).
Operator Interfaces and Team Teleoperation: Real-time sonification, parametrically or metaphorically grounded, enhances teleoperator situational awareness in multi-robot/hazard scenarios, with tradeoffs between predictability and intuitiveness depending on mapping approach (Simmons et al., 12 Jul 2024).

4. Experimental Methodologies and Results

Experimental designs in robot synesthesia vary with paradigm:

Behavioral and Ablative Benchmarking: Quantitative studies compare synesthetic fusion models to unimodal/bimodal and late-fusion baselines across metrics such as cumulative reward, task success rate, and sim-to-real generalization (Yuan et al., 2023, Li et al., 2022). Point-cloud-based tactile fusion provides the best real-world rotation performance and out-of-distribution object generalization.
User Studies for Expressiveness and Clarity: Emotion and sound-guided painting is evaluated via forced-choice human studies, demonstrating statistically significant identification accuracy (Misra et al., 2023). Music-driven swarm painting reports informal audience ratings of emotional coherence, with future formal studies planned (Cheng et al., 31 May 2025).
Perceptual/Affective Impact Assessment: Music-driven movement–sound mappings measurably increase perceived robot likability and intelligence, with movement-linked music outperforming random music in both in-person and video studies (Cuan et al., 2023). Sonification strategies for immersive teleoperation reveal that cognitive-metaphor mappings deliver more intuitive, spatially legible cues, but with higher initial cognitive load (Simmons et al., 12 Jul 2024).
Public Exhibitions as Longitudinal Field Studies: Installations like Airy record qualitative and quantitative audience reactions correlating robot strategies (sensed through visual metaphors) with intuitive spectator interpretations, demonstrating transparent communication of machine state (Chen et al., 9 Oct 2025).

5. Data Resources and Enabling Benchmarks

Comprehensive, perfectly aligned multi-modal datasets are crucial for designing and benchmarking synesthetic systems:

SynthSoM: Integrates AirSim, WaveFarer, and Wireless InSite to provide dataset-level time–space alignment of RGB, depth, LiDAR, mmWave radar, and full RF channel matrices under diverse environmental conditions. Its synchronized multi-modality enables researchers to design and test sensor–communication fusion algorithms for integrated robot perception and control (Cheng et al., 13 Jan 2025).
Open-Source Benchmarks: Datasets and codebases for visuotactile fusion, multi-sensory manipulation, and cross-modal painting are fully described and reproducible (Yuan et al., 2023, Li et al., 2022), serving as state-of-the-art testbeds.

6. Challenges, Limitations, and Future Horizons

Key open questions and challenges include:

Semantic Grounding and Cultural Specificity: Cognitive-metaphor mappings can be intuitive but are influenced by cultural expectations, requiring iterative, context-aware design (Simmons et al., 12 Jul 2024). Robustness against user variability and the need for participatory co-design are recognized.
Performance and Generalization: Sim2Real gaps in manipulation are mitigated but not eliminated by point-cloud-based synesthetic fusion; further work is needed for complex multi-stage tasks and for extracting higher-level semantic affordances from fused data (Yuan et al., 2023).
Scalable Evaluation: Artistic and emotional output evaluation remains dependent on human studies; no objective audio–image or emotion–image “fidelity” metric currently exists (Misra et al., 2023, Cheng et al., 31 May 2025).
Interactive Adaptivity: Many systems are offline or open-loop; there is a technological opportunity in closing the loop for real-time, continuous mapping in dynamic settings (e.g., streaming audio to painting, adaptive robot–user interaction) (Misra et al., 2023, Chen et al., 9 Oct 2025).
Dataset Diversity and Standardization: Ongoing need for more diverse, extensible, and standardized data to facilitate cross-method benchmarking and accelerate adoption of synesthetic architectures (Cheng et al., 13 Jan 2025).

A plausible implication is that the next phase of robot synesthesia research will converge on hybrid architectures combining robust, computationally predictable mappings for core operational streams with ecological and affective metaphors for communication and interpretability, supported by large-scale, extensible, and reproducible datasets and user studies.

7. Broader Implications

Robot synesthesia represents a paradigm shift toward embodied, interpretable, and communicative robotics, with direct relevance for:

Human-Robot Collaboration: Enhanced shared situational awareness, intuitive communication, and mutual understanding through cross-modal mappings.
Affective and Creative Robotics: New frontiers in generative art, accessibility, and expressive machines capable of conveying or eliciting human emotions through synesthetic translation (Cheng et al., 31 May 2025, Misra et al., 2023).
Transparent Autonomy and Public Oversight: Sensor-to-sense and intent-display mappings make otherwise opaque algorithms visually or auditorily legible, opening the “black box” of multi-agent and reinforcement learning systems for a wider audience (Chen et al., 9 Oct 2025).
6G and Ubiquitous Robotics: Integration of multi-modal sensor-communication data (as in SynthSoM) is foundational for next-generation, context-aware, and robustly networked robotic agents (Cheng et al., 13 Jan 2025).

Robot synesthesia thus serves as both a methodological principle for cross-modal integration and a design practice for interpretable, expressive, and adaptive interactive intelligence.