ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling
ObjRetarget integrates human videos, polyhedral hand models, and anthropomorphic constraints to achieve high-precision robot motion retargeting.
Key Findings
Methodology
ObjRetarget extracts human joint trajectories from videos as initial references, then applies anthropomorphic constraints and structured hand-object geometric models. Arm trajectories are refined via inverse kinematics and redundancy-aware optimization, ensuring natural movement. Hand contact is modeled using polytope clusters, with geometric invariants preserving contact stability. The system integrates a temporal scheduler for dual-arm coordination. Core algorithms include inverse kinematics, geometric optimization, and polytope modeling, enabling stable, generalizable manipulation.
Key Results
- On real robot tasks, ObjRetarget achieved an average success rate of 75.8%, outperforming OKAMI (61.6%) and ORION (50.8%). It significantly improved contact stability and motion naturalness across multiple dexterous tasks, demonstrating robustness to different demonstrations, object poses, and environments. Quantitative metrics showed a 30% reduction in contact errors and smoother trajectories, validating the effectiveness of structured geometric modeling and optimization.
- The multi-face polytope hand model effectively maintained multi-finger contact stability, reducing contact slip incidents. Reference trajectory-guided arm optimization improved motion accuracy and naturalness, especially under varying object initial poses. Extensive cross-scenario tests confirmed high generalization, making the approach suitable for complex, real-world manipulation scenarios. The experiments substantiate the method's potential for autonomous dexterous manipulation and human-robot collaboration.
- By combining human motion priors, geometric invariants, and structured modeling, ObjRetarget addresses the key challenge of coordinating large-scale arm movements with fine finger contact. It surpasses prior methods relying solely on deep learning or inverse kinematics by explicitly modeling contact relationships and ensuring physical plausibility. The results demonstrate significant improvements in success rates, stability, and generalization, paving the way for more dexterous, autonomous robots capable of complex manipulation tasks.
Significance
This work advances robot dexterous manipulation by integrating structured geometric models with human motion priors, overcoming limitations of purely learning-based or pose-mapping methods. Its explicit contact modeling and decoupled arm-hand control enhance stability and adaptability, crucial for real-world applications like industrial automation, service robotics, and medical assistance. The approach bridges the gap between imitation learning and geometric reasoning, offering a scalable framework for complex multi-finger tasks. It also opens avenues for autonomous skill acquisition, multi-object interaction, and human-robot collaboration, contributing to the evolution of embodied intelligence.
Technical Contribution
The paper introduces a novel decoupled arm-hand retargeting framework combining inverse kinematics, anthropomorphic constraints, and structured multi-facet polytope hand models. It innovates by explicitly modeling hand-object contact via geometric invariants, ensuring contact stability during manipulation. The integration of reference trajectory optimization with geometric constraints enhances motion naturalness and robustness. The unified temporal scheduler synchronizes dual-arm and hand motions, enabling complex, multi-contact tasks. These contributions surpass existing methods by providing a structured, generalizable approach grounded in geometric and biomechanical principles, facilitating high-fidelity dexterous manipulation.
Novelty
This is the first work to incorporate polytope cluster-based hand contact modeling within a structured, human-inspired motion retargeting framework for robots. Unlike prior approaches that rely solely on deep learning or simple pose mappings, this method explicitly preserves local contact geometry through geometric invariants, significantly improving stability and generalization. The decoupling of arm and hand motion, combined with anthropomorphic constraints, offers a new paradigm for stable, natural dexterous manipulation, especially in scenarios involving complex multi-finger interactions and object exchanges.
Limitations
- The current geometric models are primarily designed for rigid objects and may struggle with deformable or highly dynamic objects. Extending the framework to such scenarios requires more flexible geometric representations.
- Optimization processes are computationally intensive, limiting real-time deployment. Future work needs to improve efficiency for online applications.
- The system’s robustness depends on accurate perception; perception errors in pose estimation or object tracking can degrade performance. Enhancing perception robustness remains an open challenge.
Future Work
未来将结合深度学习和几何优化，提升模型的自适应能力和实时性。探索多物体、多动态环境下的多指操作，增强系统的复杂交互能力。同时，结合强化学习实现自主策略优化，推动机器人自主学习和人机协作的深度融合。
AI Executive Summary
In the realm of robotic manipulation, replicating human dexterity with high precision and stability remains a formidable challenge. Traditional approaches, relying on inverse kinematics or deep learning, often struggle to balance large-scale arm movements with fine finger control, especially in complex contact scenarios. Existing methods tend to treat the arm and hand as a monolithic system, neglecting their distinct roles and geometric differences, which leads to instability and poor generalization across diverse tasks.
This paper introduces ObjRetarget, a novel framework that bridges this gap by explicitly decoupling arm and hand motion modeling. The core innovation lies in integrating structured geometric hand models—specifically, polytope clusters—that preserve local contact invariants, with anthropomorphic constraints guiding the arm trajectories. The system begins by extracting human motion trajectories from videos, then refines these using optimization algorithms that incorporate biomechanical and task-specific constraints. The arm trajectories are optimized via inverse kinematics and redundancy-aware algorithms, ensuring natural and feasible movements. Meanwhile, the hand's multi-finger contact points are modeled as geometric entities, with invariants maintained through nonlinear optimization, enhancing grasp stability.
Experimental validation on a real dual-arm robot demonstrates significant improvements over state-of-the-art methods. The success rate across multiple tasks increased to 75.8%, with marked reductions in contact slip and trajectory errors. The approach exhibits strong generalization, adapting to different demonstrations, object poses, and environments. Its ability to coordinate large movements with precise finger contacts opens new avenues for autonomous dexterous manipulation, with applications spanning industrial automation, service robotics, and assistive devices.
Despite these advances, challenges remain. The geometric models need extension for deformable objects, and computational efficiency must be improved for real-time deployment. Perception robustness also remains critical. Future work aims to integrate learning-based adaptation, multi-object handling, and online optimization, pushing robotic dexterity closer to human-level performance and broadening its practical impact.
Deep Analysis
Background
机器人操控的研究经历了从简单的运动复制到复杂的多指操作的演变。早期工作如Inverse Kinematics（逆运动学）和运动捕捉技术，解决了大尺度运动的模仿问题，但在多指接触和复杂交互中表现不足。近年来，深度学习和强化学习被引入，提升了自主学习能力，但对接触关系和动作稳定性的建模仍有限。多面体几何模型和结构化手部建模逐渐成为研究热点，旨在提升多指操作的稳定性和泛化能力。尽管如此，如何结合人体运动特征与几何模型，实现自然、稳定的多指操控，仍是当前的研究难点。
Core Problem
核心问题在于如何在复杂交互场景中，将人类示范运动准确转化为机器人动作，特别是在多指接触稳定性和动作自然性方面。传统方法多忽视手臂大尺度运动与手指细节的差异，导致运动协调性不足。深度学习方案虽能学习复杂动作，但泛化能力有限，难以应对不同物体和环境变化。如何结合人体解剖学、几何模型和优化算法，实现结构化、对象感知的运动重定向，成为亟待解决的难题。
Innovation
本研究的创新点包括：1）提出手臂与手部运动的解耦重定向框架，利用人体解剖学约束确保动作自然；2）引入多面体簇模型，利用几何不变量保持多指接触的稳定性；3）结合参考轨迹优化和时间调度，实现多任务协调。不同于以往单一映射或端到端深度学习，该方法强调结构化几何建模和显式接触保持，显著提升泛化能力和操控稳定性。
Methodology
- �� 从视频中提取人体关节轨迹和物体点云，建立参考轨迹。• 利用逆运动学和冗余优化，结合人体解剖学约束，生成自然的手臂运动。• 采用多面体簇结构，检测多指接触，构建局部几何单元。• 通过几何不变量，优化多指接触关系，保持接触稳定。• 设计时间调度机制，协调手臂与手指运动，实现双臂同步。• 结合任务目标，优化末端执行器轨迹，确保操控精度。
Experiments
采用真实机器人平台，测试六个不同操控任务，包括物体放置、抓取、倒水等。每个任务重复20次，随机初始化物体位置，模拟实际环境。对比OKAMI和ORION方法，评估成功率、接触稳定性和运动自然度。关键指标包括成功率、接触保持时间和运动平滑性。通过 ablation 实验验证多面体模型和几何优化的贡献。系统在复杂场景下表现出优越的适应性和鲁棒性。
Results
实验显示，ObjRetarget在多任务中平均成功率达75.8%，明显优于对比方法。多指接触稳定性提升30%以上，接触误差显著降低。运动自然度和协调性增强，适应不同示范和物体姿态。多面体模型有效减少接触失稳，优化算法确保运动平滑。整体验证其在复杂操控中的优越性能，为机器人自主操控提供新思路。
Applications
该技术适用于工业自动化、服务机器人、医疗辅助等场景，尤其在复杂交互和多指操作中。只需提供人类示范视频，即可实现机器人自主学习和操作。未来结合自主策略优化，提升机器人在未知环境中的适应能力，推动智能机器人广泛应用。
Limitations & Outlook
模型对动态变化和非刚性物体的适应性有限，优化过程计算复杂，实时性不足。多物体、多接触点场景尚未充分验证，未来需提升算法效率和多目标协调能力。同时，系统对感知误差敏感，需增强鲁棒性。
Plain Language Accessible to non-experts
想象你在厨房做饭，你用手拿刀切菜，用手抓锅盖。机器人也是这样，它需要用手臂移动到正确的位置，然后用手指抓住物体。之前的机器人要么只学会大动作，要么只会细节操作，很难同时做好。这个新方法像是给机器人装上了“眼睛”和“脑袋”，让它知道什么时候用大动作，什么时候用细节，确保动作自然又稳当。它还用一种特别的几何结构，把手指和物体的接触关系变成一堆小块，确保每次抓东西都稳稳当当。这样，机器人就能像人一样灵巧地完成各种复杂任务，比如拿东西、放东西、倒水，甚至同时用两只手合作。这个技术让机器人变得更聪明、更可靠，也更像我们人类一样灵活。
ELI14 Explained like you're 14
想象你在玩拼图游戏，你需要用手把拼图块放到正确的位置。机器人以前就像是笨手笨脚的玩家，只会大致移动，或者只会拼简单的部分。现在，这个新方法就像给机器人装上了“超级眼睛”和“聪明脑袋”，让它知道什么时候用大手移动，什么时候用细手抓紧。它还用一种特别的几何图形，把手指和拼图块的接触关系变成一堆小块，确保每次拼接都稳稳当当。这样，机器人就能像人一样灵巧地完成各种复杂的任务，比如拿东西、放东西、倒水，甚至用两只手合作。这个技术让机器人变得更聪明、更可靠，也更像我们人类一样灵活。是不是很酷？
Glossary
ObjRetarget（对象感知运动重定向）
一种结合人体运动分析与几何建模的机器人运动重定向框架，旨在实现自然、稳定的多指操控。
论文提出的核心方法，用于将人类示范运动转化为机器人动作。
多面体簇（Polytope Cluster）
由多个三维多面体组成的几何结构，用于表示多指接触的局部几何关系，保持接触稳定。
用于手部接触建模，确保多指操作的几何一致性。
人体解剖学约束（Anthropomorphic Constraints）
模仿人体关节和运动特性，限制机器人运动轨迹的算法，确保动作自然合理。
用于优化手臂运动，提升动作的自然性和稳定性。
Open Questions Unanswered questions from this research
- 1 如何在动态、非刚性物体交互中保持几何模型的准确性仍待研究，尤其是在多物体、多接触点环境下的优化策略尚不完善。
Applications
Immediate Applications
工业自动化
利用ObjRetarget实现机器人在装配线上的高精度操作，减少人工干预，提高生产效率。
服务机器人
赋能机器人在家庭或医疗场景中完成复杂的多指操作，如拿取、放置和协作任务。
Long-term Vision
自主学习与人机协作
结合深度学习实现机器人自主适应新任务，推动人机协作向智能化、个性化发展。
Abstract
Learning robot dexterous manipulation from human manipulation videos requires reliably retargeting human intent to executable robot actions while maintaining stable hand-object contact, which remains a key challenge in embodied intelligence. Existing retargeting methods often ignore explicit contact modeling or rely on reinforcement learning, resulting in limited accuracy and generalization. To address this, we propose ObjRetarget, a human-to-robot motion retargeting framework for learning robot dexterous manipulation from human videos, which integrates anthropomorphic arm trajectory constraints with structured hand-object geometric modeling. For arm motion, reference trajectories extracted from human videos are used for initialization, followed by anthropomorphic constraints and redundancy-aware optimization to generate natural and accurate movements. For hand manipulation, ObjRetarget represents multi-finger contacts using polytope clusters and preserves contact structure through geometric invariants to improve stability. Experiments on real robots show that ObjRetarget improves manipulation success rates and contact stability across multiple dexterous tasks, and generalizes well to different demonstrations, object poses, and task settings.