Multi-Modal Interaction
RoboticsA comprehensive software platform for building multi-modal spatial applications that enable natural human interaction through voice, gestures, touch, and computer vision. The system provides a distributed control layer that coordinates screen movement, positioning, and content display across physical spaces, transforming environments into intelligent interfaces capable of responding to and anticipating user actions in real time.
Modality Fusion
The platform's architecture is built around a modality fusion engine that combines inputs from multiple interaction channels, speech recognition, hand tracking, gaze estimation, touch surfaces, and full-body pose detection, into unified interaction intents. Unlike systems that treat each modality independently, the fusion engine understands cross-modal references ("move that [gesture] screen to here [gesture] and play this [voice]") and resolves ambiguity by leveraging contextual awareness of the physical environment.
A probabilistic fusion model inspired by Bayesian sensor fusion techniques processes confidence-weighted hypotheses from each modality, combining them using learned priors that capture how people naturally use multiple input channels together.
Developer Platform
For developers, the platform exposes a high-level interaction API that abstracts the complexity of sensor fusion, spatial computing, and hardware coordination. Applications are defined using a declarative interaction grammar, a state machine DSL where developers define interaction states, transitions, and environment responses without requiring deep expertise in computer vision, speech processing, or robotics.
The grammar supports variable binding, temporal conditions ("if the user holds a gesture for more than 2 seconds"), spatial predicates ("when user is within 1 meter of zone A"), and multi-user scenarios. A visual editor allows interactions to be authored through direct demonstration.
Performance
The runtime handles sensor calibration, coordinate transformation between physical and virtual spaces, latency compensation, and graceful degradation when individual modalities are unavailable. All perception pipelines run in parallel on dedicated processing threads with zero-copy shared memory for frame data, achieving under 100 ms latency for gesture responses and under 300 ms for voice commands. Predictive processing begins preparing likely responses before interactions are fully resolved, ensuring the system feels immediate and natural.