arXiv subjects

Haohui Huang

Publications and source records attributed to Haohui Huang.

4 recordsLinked to original sources

arXiv2026

GenVid2Robot: From Video Generation to Robot Manipulation via Rigid-Geometric Consistency

Generated videos provide useful visual motion priors for robot manipulation, but their visual plausibility does not imply physical executability. A generated video usually lacks metric geometry, grasp grounding, robot kinematic feasibility, and execution-time feedback, which makes direct trajectory replay unreliable in real-world manipulation. This paper presents GenVid2Robot, a rigid-geometric consistency framework that converts generated video motion into executable real-robot manipulation trajectories. Given an initial RGB-D observation and a task instruction, GenVid2Robot samples task-relevant semantic anchors from the real first frame, tracks these anchors through generated video candidates, and verifies whether the resulting 2D motion can be explained by first-frame RGB-D anchors under a sparse relative $SE(3)$ model. In this way, generated videos are treated as uncertain visual motion hypotheses rather than direct robot demonstrations. Only geometrically consistent motion is transferred to the robot. The accepted relative motion is then applied to the real grasp-time TCP pose selected by mask-constrained grasping, producing a grasp-conditioned execution trajectory that is consistent with both the visual motion prior and the physical grasp configuration. To reduce execution mismatch caused by RGB-D noise, calibration residuals, and small contact-induced displacement, a bounded depth-compensation module corrects local depth-direction errors without assuming full online replanning. Real-robot experiments demonstrate that GenVid2Robot improves the reliability of generated-video-guided manipulation by grounding visual motion priors with sparse metric geometry, grasp constraints, robot feasibility checking, and bounded execution feedback.

cs.RO↗

arXiv2026

PhysV2A: Reachability-Gated and Semantic-Mask-Constrained Feasibility Completion for Video-to-Robot Manipulation

Video-based manipulation provides object-centric motion priors from human demonstrations, generated videos, or RGB-D observations, but such priors are typically embodiment-agnostic and cannot be directly executed by a specific robot. This paper presents \textbf{PhysV2A}, a reachability-gated and semantic-mask-constrained feasibility-completion framework for converting video-derived 6D object motion into robot-executable manipulation trajectories. The key idea is to treat grasp feasibility as trajectory-conditioned rather than local: each RGB-D-generated 6-DoF grasp candidate is rigidly coupled with the recovered object motion to form a grasp-conditioned TCP trajectory hypothesis. PhysV2A then performs hierarchical reachability-gated selection, where infeasible grasp--trajectory pairs are rejected by robot-centric kinematic checks and surviving candidates are ranked by downstream execution suitability. For the selected reachable trajectory, a VLM-assisted and rule-validated S-Mask identifies task-critical and relaxable Cartesian components, enabling semantic-mask-constrained manipulability refinement through redundancy-first optimization and bounded Cartesian relaxation. Real-robot experiments on four tabletop manipulation tasks show that PhysV2A improves task success over representative video-prior and IK-only baselines, reduces kinematic-feasibility failures, and produces better-conditioned trajectories with bounded semantic deviations.

cs.RO↗

arXiv2026

CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation

Humans primarily rely on walking and running to traverse complex terrains. Similarly, humanoid robots should be able to smoothly transition between walking and running while maintaining natural and stable locomotion. However, unifying gait transition and multi-terrain adaptation within a single policy remains challenging due to gradient interference between tasks and the distribution shift caused by terrain variations. Although Mixture-of-Experts (MoE) architectures can mitigate multi-skill interference, direct joint training often fails to achieve clear expert specialization. To address these challenges, we propose CoRe-MoE, a two-stage reinforcement learning framework that decouples gait generation from terrain adaptation. In the first stage, a stable locomotion policy is learned to produce natural walking and running behaviors with smooth transitions. In the second stage, a terrain-aware MoE branch is introduced, and the gating network is trained with a contrastive objective to learn structured terrain representations and promote expert specialization. The final action is obtained through weighted fusion of the base gait policy and the terrain-aware branch, enabling the policy to preserve stable locomotion while adapting to complex terrains. Extensive simulation results demonstrate that the proposed method outperforms baseline approaches in terms of success rate, locomotion stability, and multi-terrain adaptability. Furthermore, zero-shot deployment on a Unitree G1 humanoid robot validates the effectiveness of our framework, achieving robust walking and running across stairs, slopes, steps, obstacles, and unstructured outdoor terrains while maintaining accurate foothold control and dynamic stability.

cs.RO↗

arXiv2024

Unleashing the Power of LLM to Infer State Machine from the Protocol Implementation

State machines are essential for enhancing protocol analysis to identify vulnerabilities. However, inferring state machines from network protocol implementations is challenging due to complex code syntax and semantics. Traditional dynamic analysis methods often miss critical state transitions due to limited coverage, while static analysis faces path explosion issues. To overcome these challenges, we introduce a novel state machine inference approach utilizing Large Language Models (LLMs), named ProtocolGPT. This method employs retrieval augmented generation technology to enhance a pre-trained model with specific knowledge from protocol implementations. Through effective prompt engineering, we accurately identify and infer state machines. To the best of our knowledge, our approach represents the first state machine inference that leverages the source code of protocol implementations. Our evaluation of six protocol implementations shows that our method achieves a precision of over 90%, outperforming the baselines by more than 30%. Furthermore, integrating our approach with protocol fuzzing improves coverage by more than 20% and uncovers two 0-day vulnerabilities compared to baseline methods.

cs.CR↗