TL;DR
This paper introduces SynHLMA, a novel framework for synthesizing articulated object manipulation sequences from language instructions, enabling realistic hand grasp generation and prediction for articulated objects in virtual and robotic environments.
Contribution
SynHLMA is the first to integrate language, articulated object modeling, and hand manipulation sequence generation using a discrete HAOI representation and shared embedding space.
Findings
Achieves superior hand grasp sequence generation compared to state-of-the-art methods.
Successfully predicts and interpolates articulated object manipulation sequences.
Demonstrates effective robotic grasp execution from imitation learning.
Abstract
Generating hand grasps with language instructions is a widely studied topic that benefits from embodied AI and VR/AR applications. While transferring into hand articulatied object interaction (HAOI), the hand grasps synthesis requires not only object functionality but also long-term manipulation sequence along the object deformation. This paper proposes a novel HAOI sequence generation framework SynHLMA, to synthesize hand language manipulation for articulated objects. Given a complete point cloud of an articulated object, we utilize a discrete HAOI representation to model each hand object interaction frame. Along with the natural language embeddings, the representations are trained by an HAOI manipulation language model to align the grasping process with its language description in a shared representation space. A joint-aware loss is employed to ensure hand grasps follow the dynamic…
Peer Reviews
Decision·Submitted to ICLR 2026
1. The author proposes a new human–articulated-object interaction framework by LoRA training an LLM. 2. The author also proposes a new hand–articulated-object dataset that includes natural language descriptions.
Weaknesses: 1. Previous work such as HOIGPT follows a paradigm that is quite similar to the proposed approach. The paper should provide more detailed comparisons with these methods. In particular, it would be helpful to clarify whether those baselines were trained on the same dataset as SynHLMA to ensure fairness and reproducibility. 2. In the ablation studies, please specify the version of LLaMA used. While the choice of LLaMA as a comparison baseline is reasonable, there are other state-of-t
1. The dynamic modeling of hand interactions with articulated objects is well motivated and encodes both semantic intent and articulation constraints in a coherent formulation. 2. The curated dataset is likely to be useful for the community and may enable controlled studies of language-conditioned articulated manipulation. 3. The experimental evaluation in simulation is reasonably comprehensive, covering generation, prediction, and interpolation with quantitative and qualitative evidence.
1. Rendering-to-GPT caption pipeline. The paper relies on rendering sequences in Open3D and obtaining descriptions with GPT-4. The realism of Open3D renderings is limited, which may introduce a domain gap for image-to-text captioning and, in turn, for language supervision quality. 2. Assumption on fixed object base. It is unclear whether the method supports scenarios in which the articulated object’s base moves in the world. The token index \<j\> is defined in the object’s canonical space, which
1. Solid methodology integrating VQ-VAE and LoRA-tuned Vicuna. Consistent improvements on benchmarks. 2. Dataset Contribution: HAOI-Lang offers valuable large-scale multimodal data with physics-consistent interactions and generated instructions. 3. Generality: Demonstrated applicability across generation, prediction, and interpolation tasks. Showed extension to robotic dexterity transfer. 4. Clarity: Good pipeline and ablation studies on design choices (token hierarchy, VQ-VAE size, LoRA rank
1. The dataset is simulation-based, and language annotations are GPT-generated, which may limit transfer to real-world hand motions or linguistic diversity. 2. Robotic transfer is only qualitative within a simulator; no human demonstration or physical validation. 3. Comparative Scope: Comparisons are mainly against HOIGPT/Text2HOI; no baselines using diffusion or transformer-based generative models (e.g., AffordanceDiffusion, HOIDiffusion, NL2Contact). 4. Ablation Breadth: Although token-leve
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