Perception and Interaction Module

• Perception and Interaction Module (PIM) is an integrated framework that fuses multi-modal sensor inputs with action decision making in real-time applications.
• It leverages domain-specific techniques such as CVAE in soft robotics, diffusion transformers in avatar control, and PID-based feedback in nanoscale manipulation.
• Empirical studies report enhanced prediction accuracy, interaction quality, and reduced latency across robotics, video-LLMs, and nanoscale systems.

A Perception and Interaction Module (PIM) is an integrated computational architecture designed to bridge sensory data interpretation (“perception”) and action-oriented decision making or actuation (“interaction”). The term spans use cases ranging from soft robotics (Donato et al., 2024), nanoscale manipulation (0801.0678), video LLMs (Video-LLMs) (Qian et al., 6 Jan 2025), to text-driven avatar control (Zhang et al., 2 Feb 2026). While implementation details vary, a PIM consistently fuses multi-modal sensor or contextual input to produce contextually-plausible, temporally coherent actions, frequently under real-time or closed-loop constraints.

1. Structural and Computational Frameworks

The PIM paradigm is instantiated using domain-specific modalities and architectural motifs. In soft robotics (Donato et al., 2024), the PIM is realized as a conditional variational autoencoder (CVAE) that jointly encodes proprioception, tactile, and vision data into a low-dimensional latent manifold $z_t$. This latent representation captures the dynamic state and predicts future observations conditioned on both sensory history and control actions.

In grounded avatar generation (Zhang et al., 2 Feb 2026), PIM operates as a diffusion transformer. It explicitly extracts a structured scene representation from a reference image and text prompt. The module then synthesizes a temporal sequence of actionable motion representations, aligned with environmental geometry and linguistic task specification.

For nanoscale interfacing (0801.0678), the PIM materializes as a real-time, closed-loop pipeline coupling physical nanosensors/actuators with multi-sensory human-machine interfaces (visual, auditory, haptic), transforming probe-level data into immersive, manipulable virtual scenes.

In streaming video-LLMs (Qian et al., 6 Jan 2025), Dispider’s PIM employs a scene-based streaming processor. Real-time video frames are semantically clustered into nonuniform clips, encoded, and stored in memory pools for downstream decision and interaction logic.

2. Perceptual Fusion and Representation Learning

Across applications, the PIM’s perception subsystem is engineered to efficiently aggregate diverse input streams, enabling robust state inference and cross-modal generation.

In (Donato et al., 2024), sensory fusion is performed by a neural encoder $q_\phi(z|S_t)$ mapping high-dimensional state $S_t = \{q_t, f_t, v_t\}$ into a compressed distribution over latent space $z_t$. The module can hallucinate missing modalities (e.g., tactile signals from purely visual and proprioceptive cues) by decoding projected latents, exploiting inter-modal causal structure.

In InteractAvatar (Zhang et al., 2 Feb 2026), a single image is encoded by VAE to provide spatial context; textual commands and task embeddings are concatenated to form a cross-modal context vector. Perception is tightly coupled to detection-style self-supervision: the module must localize objects and avatar keypoints for physically plausible motion planning.

Streaming video-LLMs (Qian et al., 6 Jan 2025) maintain a buffer of scene embeddings (SigLip, CLIP) and segment input into semantically coherent clips via cosine similarity thresholding. These memory pools encode the ongoing context, maintaining temporal structure suitable for retrieval-based interaction.

For nanoscale perception (0801.0678), hardware signals are modeled, filtered, and feature-extracted (thresholding, slope estimation, SVM-based contact classification) before multimodal rendering.

3. Interaction Logic and Control Synthesis

Interaction within a PIM follows diverse methodologies, reflecting the actuation space of the underlying domain:

• Soft robotic PIMs condition their generative decoder $p_\theta(S_{t+1}|z_t, a_t)$ on latent states and explicit action $a_t$, synthesizing predictions across sensory modalities for the next timestep. This enables active planning under uncertainty and closed-loop feedback, leveraging the causal relationship between history and consequence (Donato et al., 2024).
• Avatar PIMs (Zhang et al., 2 Feb 2026) utilize a DiT-style diffusion model. Through iterative transformer blocks—each with multi-head self-attention, text cross-attention, and image cross-attention—PIM incrementally refines latent motion representations, eventually generating keypoint/bounding box trajectories via VAE decoding. Parallel injection of PIM residuals into the video generation module ensures video output remains coupled to the interaction plan.
• Nano-manipulation PIMs implement classical feedback controllers (PID loops) augmented by higher-level finite state machines managing approach, force stabilization, contact, and retraction (0801.0678). Interaction is rendered at human scale via force scaling and high-frequency haptic feedback, closing the loop between perception and user-enacted intention.
• Streaming LLM systems (Qian et al., 6 Jan 2025) decouple perception, decision, and interaction modules. The decision logic (binary classification head over tokenized sequence with ⟨TODO⟩ markers) asynchronously triggers the reaction module. The retrieval-augmented reaction pipeline queries contextually-relevant clips, composes prompts, and generates autoregressive responses, while perception continues uninterrupted.

4. Training Protocols and Loss Functions

PIM implementations adopt tailored loss landscapes to ensure faithful multi-modal prediction, actionable planning, and physically plausible interaction.

• CVAE-based PIMs (Donato et al., 2024) optimize the ELBO:

$$\mathcal{L}_{t} = \mathbb{E}_{q_\phi(z|S_t)}\left[\log\,p_\theta(S_{t+1}\mid z, a_t)\right] - \mathrm{KL}\left(q_\phi(z|S_t)\|p(z)\right)$$

with reconstruction loss supplied by MSE over the reconstructed modalities.

• Diffusion-transformer PIMs (Zhang et al., 2 Feb 2026) minimize a unified flow matching objective $L_{FM}$ to enforce consistency of the learned vector field with diffusion dynamics, augmented by motion MSE, detection error (GIoU + $\ell_1$), and temporal smoothness regularization.
• Video-LLM PIMs (Qian et al., 6 Jan 2025) apply binary cross-entropy for decision head training, KL loss for multi-hop clip retrieval alignment, and standard sequence generation loss for LLM response quality.
• Nano-PIMs (0801.0678) combine model-based state estimation, digital filtering, machine learning-based contact classification, and PID controller stability constraints. No learned parameters are reported, but performance is bound by system noise floor, latency, and the model-based estimation accuracy.

5. Empirical Validation and Performance Characteristics

PIM performance is quantitatively and qualitatively validated via application-specific benchmarks, ablation studies, and user studies.

• In multi-modal soft robots (Donato et al., 2024), cross-modal prediction WMAPE falls from >100% (proprio alone) to ≈20–40% (vision+proprio input). Latent dimension ablations show optimal accuracy with $z\approx64$.
• For avatar generation (Zhang et al., 2 Feb 2026), GroundedInter benchmark results demonstrate that the presence of PIM substantially improves human–object interaction metrics: VLM-QA from 26.19 to 28.89, Hand Quality (HQ) from 0.711 to 0.925, and Pixel-level Interaction (PI) from 0.685 to 0.780. Ablation of perception-specific training leads to metric drops, confirming the essential role of explicit environment modeling.
• In Dispider (Qian et al., 6 Jan 2025), PIM-enabled perception/interaction achieves a 53.1% overall streaming score (vs. <36% for prior work), with reaction times for LLM-generated responses (~2–5 s) remaining decoupled from real-time perception.
• Nanoscale system PIMs (0801.0678) report <1 ms sensor-to-haptic latency, with high-fidelity force rendering and successful public engagement (5749 users, 2 min average manipulation time), demonstrating the viability of closed-loop, multi-sensory perception-action mapping.

6. Typical Computational Pipelines and Equations

PIM architectures are typically characterized by the following signal and computation flows:

• Soft robotics (Donato et al., 2024):

$$z_t \sim q_\phi(z|S_t);\ \hat S_{t+1} \sim p_\theta(S|z_t, a_t)$$

$$L = MSE[\hat S_{t+1}, S_{t+1}] + \beta\cdot KL[ q_\phi(z|S_t) \parallel N(0,I) ]$$

• Avatar PIMs (Zhang et al., 2 Feb 2026):

1. Encode image, text, task: $z_I$, $f_{text}$, $f_{task}$.
2. Initialize latent motion $Z_0$.
3. For each timestep, apply blockwise self/cross-attention and MLP layers.
4. Sample motion sequence via:

   $$z_{\tau-1} = z_\tau + \eta\cdot u_\theta(z_\tau, \tau, C)$$

5. Decode via VAE for $m_t$ sequence.

• Streaming Video-LLMs (Qian et al., 6 Jan 2025): Scene embedding, boundary detection, mean memory update, binary decision on “respond”, asynchronous reaction with clip-retrieval and LLM decoding.
• Nano-PIMs (0801.0678): Signal models:

$$I(d) = I_0 e^{-2\kappa d}\,,\ F = kx;\ x_n = \sqrt{k_B T / k}$$

PID law:

$$u(t) = K_p e(t) + K_i \int e(\tau)d\tau + K_d \frac{d e(t)}{dt}$$

7. Challenges, Limitations, and Future Directions

PIMs face domain-specific and cross-cutting obstacles:

• Soft robotics: Information bottlenecks in unimodal prediction, latent-overfitting with excessive dimension, and loss of detail under too-aggressive compression (Donato et al., 2024).
• Avatar/interaction generation: Balancing perception-driven planning against photorealistic synthesis (the "control–quality dilemma"), and the need for explicit environmental grounding to avoid implausible motion (Zhang et al., 2 Feb 2026).
• Nano-actuation: Noise floors restrict the minimum force perceivable, and current implementations are predominantly one-dimensional. Future work targets multi-DOF extension, chemical sensing, adaptive control (MPC), and the quantification of human learning/adaptation (0801.0678).
• Streaming systems: The trade-off between perception update rates, interaction timeliness, and computational/latency budgets remains a persistent systems problem (Qian et al., 6 Jan 2025).

A general implication is that as environments, input modalities, and actuator repertoires become increasingly complex, modular, and explicitly disentangled PIM frameworks become essential for scalable, interpretable, and robust perception–interaction cycles.