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Selected work

Representative Papers

Anticipate & Act: Integrating LLMs and Classical Planning for Efficient Task Execution in Household Environments†

May 13, 2024IEEE International Conference on Robotics and Automation

To address low multi-task execution efficiency of assistive agents in domestic environments, this paper proposes an LLM-driven joint task planning framework. It leverages large language models (LLMs) with few-shot prompting to achieve zero-shot high-level task anticipation, then uniformly encodes the anticipated multi-task set as a PDDL goal for classical planning—specifically, the FF Planner—to generate a synergistically optimized, fine-grained action sequence. This work establishes the first seamless integration of LLM-based task anticipation with symbolic classical planning, enabling cross-task action coordination without any training data. Evaluated in the VirtualHome simulation environment, the framework reduces task completion time by 31% compared to serial single-task execution baselines, demonstrating its effectiveness in action reuse, temporal optimization, and resource coordination.

7 citations1 influentialRead paper

Teleoperated Omni-Directional Dual Arm Mobile Manipulation Robotic System With Shared Control for Retail Store

Oct 06, 2024IEEE International Conference on Systems, Man and Cybernetics

This work proposes a retail-oriented omnidirectional dual-arm mobile manipulation system designed to address the limited adaptability of autonomous mobile robots in dynamic retail environments, where new products and scenarios frequently emerge. The system integrates an omnidirectional mobile base, dual-arm cooperative control, and virtual reality (VR)-based motion capture, enabling both single-arm and bimanual manipulation. It employs heterogeneous end-effectors and combines VR teleoperation with a human–robot shared control strategy to enhance versatile grasping capabilities across a wide range of consumer goods. Experimental validation in a simulated retail setting demonstrates the system’s ability to efficiently and flexibly handle diverse everyday items, significantly improving its practicality and adaptability in real-world retail applications.

3 citationsRead paper

Grounded and Faithful P&ID Reasoning: Constraining Vision-Language Models with Recovered Evidence Graphs

Sep 05, 2026

Piping and Instrumentation Diagrams (P&IDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what connects to what. Vision-language models describe these sheets fluently, yet they often invent or miss process connections---and an invented or missed link can reverse an isolation or reachability call, so a plant decision cannot trust a fluent answer that was never checked against the linework. We instead recover an explicit graph of the drawing---its symbols, the process connections between them, and the tags that name them---and then require the model to answer only by querying that graph through seven read-only operators, so a topology claim is returned only when it cites the query results that support it. On TopoPID-VQA, a new suite of 3000 topology questions over these sheets, Graph-Grounded Harness (Ours) raises exact match accuracy from 36.7--41.3% under image-only prompting to 74.3--76.0% for Qwen3-VL-4B, Qwen3-VL-8B, and Gemma-4-E4B. It does so on an imperfect substrate: on Digitize-PID dataset the recovered graph scores F1 0.742 on exact process connections, and 0.801 once symbols and tags are pooled in. The residual errors track that gap---grounding pays off where the recovered graph is right, and perception error still breaks topology questions where it is not.

0 citationsRead paper

On-the-go Forgetting without Explicit Unlearning via ERASE

Sep 05, 2026Trans. Mach. Learn. Res.

Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization, and limited scalability. In this work, we introduce ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forgetting that suppresses the observable influence of private data without modifying model weights. ERASE leverages structured, class-conditioned input perturbations to induce selective forgetting during inference, eliminating the need for retraining, fine-tuning, or model copies. We rigorously characterize sufficient conditions when ERASE provably achieves functional forgetting of designated subclasses while preserving predictions across other subclasses within the same superclass. This analysis offers a principled foundation for inference-time forgetting under mild regularity assumptions. Across diverse architectures and benchmark datasets, ERASE maintains the best observed balance between forgetting efficacy, computational efficiency, and retention fidelity over recent unlearning-based methods. By reimagining data removal as forgetting without unlearning, our work establishes a scalable, regulation-aligned pathway for continual, privacy-conscious learning.

0 citationsRead paper
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Latest Papers

Grounded and Faithful P&ID Reasoning: Constraining Vision-Language Models with Recovered Evidence Graphs

Sep 05, 2026

Piping and Instrumentation Diagrams (P&IDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what connects to what. Vision-language models describe these sheets fluently, yet they often invent or miss process connections---and an invented or missed link can reverse an isolation or reachability call, so a plant decision cannot trust a fluent answer that was never checked against the linework. We instead recover an explicit graph of the drawing---its symbols, the process connections between them, and the tags that name them---and then require the model to answer only by querying that graph through seven read-only operators, so a topology claim is returned only when it cites the query results that support it. On TopoPID-VQA, a new suite of 3000 topology questions over these sheets, Graph-Grounded Harness (Ours) raises exact match accuracy from 36.7--41.3% under image-only prompting to 74.3--76.0% for Qwen3-VL-4B, Qwen3-VL-8B, and Gemma-4-E4B. It does so on an imperfect substrate: on Digitize-PID dataset the recovered graph scores F1 0.742 on exact process connections, and 0.801 once symbols and tags are pooled in. The residual errors track that gap---grounding pays off where the recovered graph is right, and perception error still breaks topology questions where it is not.

0 citationsRead paper

On-the-go Forgetting without Explicit Unlearning via ERASE

Sep 05, 2026Trans. Mach. Learn. Res.

Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization, and limited scalability. In this work, we introduce ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forgetting that suppresses the observable influence of private data without modifying model weights. ERASE leverages structured, class-conditioned input perturbations to induce selective forgetting during inference, eliminating the need for retraining, fine-tuning, or model copies. We rigorously characterize sufficient conditions when ERASE provably achieves functional forgetting of designated subclasses while preserving predictions across other subclasses within the same superclass. This analysis offers a principled foundation for inference-time forgetting under mild regularity assumptions. Across diverse architectures and benchmark datasets, ERASE maintains the best observed balance between forgetting efficacy, computational efficiency, and retention fidelity over recent unlearning-based methods. By reimagining data removal as forgetting without unlearning, our work establishes a scalable, regulation-aligned pathway for continual, privacy-conscious learning.

0 citationsRead paper