Multimodal Perception Cross-Validation Mechanism

• The paper introduces a bidirectional mechanism that fuses vision and force feedback to dynamically correct perceptual errors during robotic task execution.
• It couples Neuro-Symbolic Task and Motion Planning with continual learning, using neural predicates for real-time state verification and adaptive replanning.
• Experimental results in EV battery disassembly show a jump from 81.68% to 100% task success and a two-thirds reduction in perceptual replans.

A multimodal perception cross-validation mechanism is an architectural and algorithmic principle in embodied intelligence and robotic systems whereby distinct sensory streams—such as vision and force—are integrated with bidirectional reasoning to boost robustness and adaptability, especially under real-world uncertainty. The mechanism supports dynamic, context-dependent verification and continual refinement by enabling sensory information streams to mutually validate and, when necessary, correct each other during perception, action planning, and execution. In the context of disassembly and autonomous robotics, this approach addresses the frequent problem of unreliable perception caused by environmental noise, occlusion, or variable contact conditions by tightly coupling a Neuro-Symbolic Task and Motion Planning (TAMP) system with continual learning.

1. Mechanism Structure: Bidirectional, Modality-Cross Validated Reasoning

Central to this paradigm is the simultaneous use of two distinct perception modalities with complementary strengths—concretely, vision (providing spatial/structural context) and force feedback (offering reliable contact-state validation under perturbation). The mechanism proceeds as follows:

• Primary estimation: Vision (modality A) is used to estimate the target state (e.g., the pose of a screw in a disassembly task) and to construct a high-level symbolic state representation via neural predicates, suitable for task planning with PDDL-based symbolic planners.
• Action execution and verification: After planning, the system executes actions (e.g., Insert) and uses real-time force feedback (modality B) to verify whether critical state predicates (such as 'socketed') are now true. If verification fails, force-based perception is invoked to re-estimate the target state and trigger dynamic replanning if the state is inconsistent.

This interplay ensures that real-time perception errors can be detected and corrected via cross-modal feedback, establishing a closed-loop validation mechanism at the perception-action interface.

2. Integration in Neuro-Symbolic Task and Motion Planning (TAMP)

Within Neuro-Symbolic TAMP, perceptual inputs are abstracted into a set of neural predicates $\{P_i\}_{i=1}^m$—binary or multiclass symbolic descriptions like have_coarse_pose, near_screw, or socketed. The planning module generates an ordered primitive list (e.g., $$prim\_{list} = \{a_{p_1}, a_{p_2}, \ldots, a_{p_m}\}$$) from the initial state $S_0$ to a goal $S_G$. At each primitive, before execution, predicate verification via current sensor streams ensures preconditions are met; post-action, the system checks that the intended effect holds. If not, replanning and cross-modal re-evaluation are triggered.

This cross-validation tightly couples TAMP with perception in both forward (execution-centered) and backward (learning-centered) flows, guaranteeing that perceptual errors do not escalate through the plan execution trajectory.

3. Backward Learning Flow: Continual Correction and Predicate Update

After task execution, a backward learning flow is used for self-optimization, leveraging multimodal perception cross-validation for the following:

a) Perception Estimation Correction

If force feedback reveals a mismatch (for example, the socketed predicate is false after Insert), the true pose from force is recorded as

$\mathbf{P}_{\text{true}} = [x, y, z, \theta]$

and the vision-estimated (or prior modality) pose is

$\mathbf{P}_B = f_B(I_B; \theta_B)$

With the loss function

$$L_B = \frac{1}{2} \|\mathbf{P}_{\text{true}} - \mathbf{P}_B\|^2_2$$

the perception module’s parameters are corrected by gradient descent: $$\theta_B^{(t+1)} = \theta_B^t - \eta \nabla_{\theta_B} L_B$$

b) Neural Predicate Correction

For symbolic reasoning, if a misjudgment is ascertained, the predicate network outputs

$g_i(x; \phi_i) = [p_i^0, p_i^1]$

with confidence $c_i = \max(p_i^0, p_i^1)$. The least confident predicate $k = \arg\min_i c_i$ is selected, its label $\hat{y}_k = 1 - y_k$ (flipped), and the classifier $\phi_k$ is updated via the cross-entropy loss: $$L_k = -[\hat{y}_k \log p_k^1 + (1 - \hat{y}_k) \log p_k^0]$$ Gradient descent applies: $$\phi_k^{(t+1)} = \phi_k^t - \eta \nabla_{\phi_k} L_k$$ All corrections are placed in a continual buffer for periodic, incremental retraining—thus ensuring the system learns from its mistakes.

4. System Adaptation and Robustness

The dual-flow structure—forward for execution-time dynamic error detection and backward for batch-based self-refinement—renders the system capable of both immediate adaptation and long-term learning. The cross-validation mechanism ensures that perception errors, whether from vision in poor lighting or force sensors during dynamic interaction, are dynamically compensated by the complementary modality and ultimately propagated as ground-truth for model correction.

The forward flow minimizes cascading errors during live deployment by triggering prompt correction and replanning, while the backward flow continually reduces the frequency of such errors through detection and parameter correction, thus resulting in robust and increasingly autonomous operation in dynamic environments.

5. Experimental Results: Quantitative Impact

In the electric vehicle battery disassembly domain, empirical evaluation demonstrates the effectiveness of this cross-validation mechanism:

Metric	Before Cross-Validation	After Cross-Validation
Task Success Rate	81.68%	100%
Average Perceptual Replans/Task	3.389	1.128

These metrics indicate that mutual cross-validation of vision and force greatly increases robustness—enabling perfect task completion in challenging, dynamic scenarios and reducing the need for disruptive replanning cycles by two-thirds.

6. Practical and Theoretical Implications

By reconciling discrepancies between multiple modalities in both execution and learning, the outlined multimodal cross-validation mechanism establishes a resilient foundation for autonomous robotics in real-world, unstructured environments. Key implications include:

• Dynamic error compensation: Critical for tasks with substantial environmental variability and sensor noise.
• Symbolic-perceptual synergy: Abstraction layers via neural predicates allow high-level planning while supporting low-level reality checks.
• Closed-loop continual learning: The mechanism serves not only for error correction but as a continual source of training data for perceptual modules and symbolic groundings.

This approach represents a paradigm shift versus traditional unimodal or serial validation schemes, providing foundational principles for robust, adaptable, and self-improving industrial AI systems operating under uncertainty (He et al., 14 Sep 2025).