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Shenyang Institute of Automation

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Research library51linked papers
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Selected work

Representative Papers

Never-Ending Behavior-Cloning Agent for Robotic Manipulation

Mar 01, 2024

Embodied robots struggle with 3D scene understanding and human-level task generalization in unstructured environments due to reliance on multimodal observations. Method: This paper proposes a lifelong language-conditioned behavioral cloning framework tailored for real-world scenarios. It introduces the first lifelong behavioral cloning paradigm; designs a skill-sharing semantic rendering and representation distillation module to mitigate 3D representation blind spots; and develops a skill-specific evolutionary planner enabling human-like incremental knowledge embedding in a low-rank latent space. Contribution/Results: Evaluated on a newly established lifelong manipulation benchmark, the method significantly outperforms state-of-the-art approaches. The code, dataset, and visualization results are publicly released, demonstrating strong cross-task sequential adaptability and robustness to continual learning.

5 citationsRead paper

Observation Design for Certified Control Authority: Projection--Estimability Separation and Active-Face Equivalence

Sep 05, 2026

A sound runtime admission gate executes only actions it can certify, and certifies only what its observations support. This paper asks how observations should be designed to maximize the set of actions that can be safely admitted, and shows the question is not a re-vocabulary of classical design problems. First, a projection--estimability separation: decomposing a constraint normal as $c=c_{\mathrm{Range}}+c_{\ker}$ relative to an information matrix, two-point discrimination along $c$ becomes arbitrarily reliable as the budget grows whenever $c_{\mathrm{Range}}\neq 0$, while robust admission of an action with normal $c$ is impossible at every budget whenever $c_{\ker}\neq 0$; such mixed directions are generic at any deficient rank, and at full rank the decoupling is bounded by the Kantorovich ratio and diverges with the condition number. Discrimination-optimal designs, being corner solutions of a linear criterion, land in exactly this regime. Second, an active-face equivalence theorem: the permissiveness-optimal design is $L$-optimal for a target matrix generated endogenously by the action faces that become certification bottlenecks, weighted inversely by their remaining slack; single-face collapse recovers $c$-optimal and goal-oriented design exactly, and a Caratheodory argument yields a bottleneck certificate of at most $r(r+1)/2+1$ faces. Around these we assemble exact certifiability per convex contract mode, for which $\kappa\approx 3.29$ is derived rather than calibrated, the $\sqrt{r}$ price of contract-agnostic design, an information-to-slack transfer theorem with a curvature-budget corollary, and a two-part audit in which a design meeting every margin requirement still leaves an action face at a certification cost above ten times its testing cost, in every probe library tested.

1 citations1 influentialRead paper

Recovering Process Variables from Industrial Network Traffic via Search-Based Optimization

Aug 17, 2026

This study addresses the challenges of incomplete process variable observation in industrial cyber-physical systems and the failure of existing reverse engineering methods under mixed traffic and long-payload conditions. We propose PVParser, a novel framework that pioneers a search-optimization-based paradigm for non-sequential field segmentation. By formulating variable recovery as an optimization problem and integrating periodic pattern detection with an improved Monte Carlo Tree Search, PVParser effectively mitigates the error propagation inherent in traditional sequential inference. Experimental evaluations across three industrial datasets demonstrate that PVParser significantly outperforms six state-of-the-art baselines in both accuracy and F1-score, achieving high-precision semantic recovery of protocol fields.

0 citationsRead paper
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Latest Papers

Observation Design for Certified Control Authority: Projection--Estimability Separation and Active-Face Equivalence

Sep 05, 2026

A sound runtime admission gate executes only actions it can certify, and certifies only what its observations support. This paper asks how observations should be designed to maximize the set of actions that can be safely admitted, and shows the question is not a re-vocabulary of classical design problems. First, a projection--estimability separation: decomposing a constraint normal as $c=c_{\mathrm{Range}}+c_{\ker}$ relative to an information matrix, two-point discrimination along $c$ becomes arbitrarily reliable as the budget grows whenever $c_{\mathrm{Range}}\neq 0$, while robust admission of an action with normal $c$ is impossible at every budget whenever $c_{\ker}\neq 0$; such mixed directions are generic at any deficient rank, and at full rank the decoupling is bounded by the Kantorovich ratio and diverges with the condition number. Discrimination-optimal designs, being corner solutions of a linear criterion, land in exactly this regime. Second, an active-face equivalence theorem: the permissiveness-optimal design is $L$-optimal for a target matrix generated endogenously by the action faces that become certification bottlenecks, weighted inversely by their remaining slack; single-face collapse recovers $c$-optimal and goal-oriented design exactly, and a Caratheodory argument yields a bottleneck certificate of at most $r(r+1)/2+1$ faces. Around these we assemble exact certifiability per convex contract mode, for which $\kappa\approx 3.29$ is derived rather than calibrated, the $\sqrt{r}$ price of contract-agnostic design, an information-to-slack transfer theorem with a curvature-budget corollary, and a two-part audit in which a design meeting every margin requirement still leaves an action face at a certification cost above ten times its testing cost, in every probe library tested.

1 citations1 influentialRead paper

Recovering Process Variables from Industrial Network Traffic via Search-Based Optimization

Aug 17, 2026

This study addresses the challenges of incomplete process variable observation in industrial cyber-physical systems and the failure of existing reverse engineering methods under mixed traffic and long-payload conditions. We propose PVParser, a novel framework that pioneers a search-optimization-based paradigm for non-sequential field segmentation. By formulating variable recovery as an optimization problem and integrating periodic pattern detection with an improved Monte Carlo Tree Search, PVParser effectively mitigates the error propagation inherent in traditional sequential inference. Experimental evaluations across three industrial datasets demonstrate that PVParser significantly outperforms six state-of-the-art baselines in both accuracy and F1-score, achieving high-precision semantic recovery of protocol fields.

0 citationsRead paper

Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation

Jul 17, 2026

This work addresses the limited generalization of robotic imitation learning in unseen tasks, which stems from data scarcity and environmental discrepancies. To overcome this, the authors propose the DAMI framework, which leverages meta-learning to construct a shared skill space and introduces three key components: a Visual-Motor Trajectory (VMT) module to model spatiotemporal dynamics, an Unpaired Unified Task (U2T) module to fuse multimodal observations without requiring paired demonstrations, and a Task-Conditioned Feature Modulation (TCFM) mechanism that emphasizes task-essential features over superficial cues. DAMI enables rapid adaptation to new tasks with only a few samples and no need for task-aligned demonstrations. Experimental results demonstrate that DAMI significantly outperforms existing methods in both simulation and real-world settings, achieving strong performance on seen tasks and exceptional generalization to unseen tasks after minimal fine-tuning.

0 citationsRead paper