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Mengzhen Liu

7 accepted papers

2026

MotionTrans: Human VR Data Enable Motion-Level Learning for Robotic Manipulation Policies

ICRA 2026poster

Scaling real robot data is a key bottleneck in imitation learning, leading to the use of auxiliary data for policy training. While other aspects of robotic manipulation such as image or language understanding may be learned from internet-based datasets, acquiring motion knowledge remains challenging…

2026

SaPaVe: Towards Active Perception and Manipulation in Vision-Language Action Models for Robotics

CVPR 2026

Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven perception actively with robust, viewpoint-invariant execution accordingly. To this end, we propose SaPaVe, an end-to-end framework that jointly learns these

Cited by 0SourceScholar
2026

Unifying Diffusion and Autoregression for Generalizable Vision-Language-Action Model

ICLR 2026poster

A central objective of manipulation policy design is to enable robots to comprehend human instructions and predict generalized actions in unstructured environments. Recent autoregressive vision-language-action (VLA) approaches discretize actions into bins to exploit the pretrained reasoning and gene…

Cited by 0SourceScholar
2025

Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs

NeurIPS 2025poster

In multimodal large language models (MLLMs), the length of input visual tokens is often significantly greater than that of their textual counterparts, leading to a high inference cost. Many works aim to address this issue by removing redundant visual tokens. However, current approaches either rely o…

Cited by 0SourcecodeScholar
2025

CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World

RSS 2025poster

Achieving human-level dexterity in robots is a key objective in the field of robotic manipulation. Recent advancements in 3D-based imitation learning have shown promising results, providing an effective pathway to achieve this goal. However, obtaining high-quality 3D representations presents two key…

Cited by 2PDFScholar
2025

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

RSS 2025poster

Developing robust and general-purpose manipulation policies is a key goal in robotics. To achieve effective generalization, it is essential to construct comprehensive datasets that encompass a large number of demonstration trajectories and diverse tasks. Unlike vision or language data, which can be…

Cited by 20PDFScholar
2024

RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation

NeurIPS 2024poster

A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots can handle a range of basic tasks, they still face challenges in two areas: (1) insufficient reasoning ability to tackle…

Cited by 5SourcePDFScholar