General In-Hand Object Rotation with Vision and Touch
RotateIt employs multimodal perception with vision and touch, trained in simulation, to enable multi-axis in-hand object rotation without fine-tuning in real-world.
Key Findings
Methodology
The system integrates a privileged oracle policy trained with ground-truth object shape and physical parameters, optimized via PPO. A visuotactile transformer fuses multimodal sensory sequences—visual, tactile, proprioceptive—to infer latent object representations during deployment. The training involves simulating diverse objects, collecting depth, contact, and proprioception data, then training the transformer to regress privileged encodings. This approach enables direct transfer to real-world without fine-tuning, leveraging a combination of PointNet for shape encoding and transformer-based temporal modeling. The policy controls a dexterous AllegroHand to perform multi-axis rotation, with sensory inputs processed in real-time.
Key Results
- In simulation, RotateIt achieved a rotation reward of 140.9 on z-axis, surpassing prior methods like Hora (99.83) by over 40%. Incorporating shape information via point clouds improved performance by over 40% across axes. In real-world tests, success rates increased by 20%, with out-of-distribution object generalization improving from 78% to 90%. The multimodal approach reduced rotation time by 20% and increased stability, especially on irregular objects. The transformer-based model outperformed convolutional alternatives in sequence modeling, demonstrating robustness to sensor noise and environmental variability.
- Ablation studies confirmed that visual and tactile inputs significantly boost performance, with combined modalities outperforming proprioception alone by 50%. The point cloud encoding notably enhanced handling complex shapes, especially for objects with high aspect ratios. Out-of-distribution evaluation showed performance drops of only 8-15%, indicating strong generalization. The learned latent representations could predict 3D shapes accurately, validating the effectiveness of the privileged information inference during real-world deployment.
Significance
This work advances the state-of-the-art in dexterous in-hand manipulation by integrating multimodal perception and deep sequence modeling, enabling robots to perform complex multi-axis rotations with high robustness and generalization. It addresses longstanding challenges in perception fusion, sim-to-real transfer, and manipulation of diverse objects, paving the way for autonomous robots in manufacturing, logistics, and service industries. The approach reduces reliance on precise object models and extensive real-world data, offering a scalable solution for versatile robotic manipulation.
Technical Contribution
The paper introduces a novel visuotactile transformer architecture that models temporal multimodal data for real-time inference of object properties. It combines PointNet-based shape encoding with sequence modeling, enabling accurate estimation of physical attributes from noisy sensor data. The training pipeline includes privileged oracle policy optimization in simulation, knowledge distillation to the multimodal model, and direct sim-to-real transfer without fine-tuning. The system demonstrates superior robustness and generalization over existing methods relying solely on proprioception or single modalities.
Novelty
This is the first work to unify vision, touch, and proprioception via a transformer for in-hand multi-axis object rotation. Unlike prior approaches limited to single-axis or relying solely on shape or pose, this method leverages multimodal temporal data for comprehensive physical understanding. The integration of PointNet shape encoding with a sequence transformer for real-time inference and direct sim-to-real transfer marks a significant innovation, opening new avenues in dexterous robotic manipulation.
Limitations
- The system's performance degrades under high sensor noise or occlusion, especially in cluttered environments, requiring further robustness improvements.
- Dependence on simulated physics accuracy and segmentation quality may limit transferability to highly dynamic or unstructured real-world scenarios.
- Computational complexity of transformer inference may hinder real-time operation in high-speed tasks, necessitating optimization.
Future Work
Future efforts will focus on enhancing sensor robustness, reducing computational costs, and extending capabilities to more complex manipulation tasks such as object reorientation and assembly. Incorporating adaptive learning strategies and active perception could further improve performance in unstructured environments. Additionally, integrating more diverse sensory modalities like force/torque sensing and tactile arrays will broaden application scope.
AI Executive Summary
In the realm of robotic manipulation, achieving dexterous in-hand object rotation remains a formidable challenge, especially when dealing with diverse shapes, materials, and orientations. Traditional approaches often rely on pre-defined models or single sensing modalities, which limit their adaptability and robustness. Addressing these limitations, this research introduces RotateIt—a system that leverages multimodal perception, combining vision and touch, to enable robots to perform multi-axis object rotation seamlessly.
The core innovation lies in training a privileged oracle policy within simulation, utilizing ground-truth object shape and physical parameters, optimized through reinforcement learning. This oracle policy serves as a performance benchmark, guiding the development of a real-world deployable policy. To bridge the sim-to-real gap, the authors design a visuotactile transformer that fuses sequences of visual, tactile, and proprioceptive data, inferring latent object representations critical for manipulation. This transformer-based approach captures temporal dependencies and complex multimodal correlations, outperforming traditional convolutional models.
Extensive experiments demonstrate that RotateIt surpasses prior methods like Hora in simulation, achieving over 40% higher rotation rewards. When transferred to real robots, it maintains high success rates across a variety of objects, including irregular shapes and out-of-distribution cases, with performance improvements of up to 22%. The inclusion of shape encoding via point clouds significantly enhances handling complex geometries. The system’s ability to operate without fine-tuning underscores its robustness and practicality.
This work marks a significant step forward in robotic dexterity, enabling more versatile and reliable in-hand manipulation. It opens pathways for robots to perform complex tasks in unstructured environments, such as assembly lines, warehouses, and service settings. Future directions include improving sensor robustness, reducing computational overhead, and expanding multimodal sensing to include force feedback, further broadening the scope of autonomous robotic manipulation.
Deep Analysis
Background
机器人在手中操控技术经历了从基于模型的规划到深度学习的快速发展。早期方法如基于动力学模型的运动规划，在特定任务中表现良好，但难以应对复杂环境和多样物体。近年来，深度强化学习和模仿学习成为主流，代表性工作包括OpenAI的Dexterous Manipulation和Google的RL Manipulation。尽管如此，融合多模态感知、实现迁移学习和复杂物体泛化仍是挑战。多模态感知融合尤其关键，因为单一模态难以捕获物体的完整信息，限制了操控的鲁棒性。
Core Problem
在多轴、多形状、多材质的物体旋转任务中，现有方法多依赖预定义模型或单一感知模态，面对真实环境中的感知噪声和变化，表现出明显局限。如何融合视觉、触觉和本体信息，实时推断物体的形状和物理属性，成为提升操控鲁棒性和泛化能力的核心难题。此外，模拟训练到实地迁移的效率和效果也是亟待解决的问题。
Innovation
本研究提出多模态感知融合框架，结合模拟中的优越策略与知识蒸馏，利用Transformer模型实现多模态时间序列建模，推断物体的隐含属性。具体创新包括：1）设计了visuotactile transformer，用于融合视觉、触觉和本体感知信息；2）引入点云编码，增强复杂形状理解；3）在模拟中训练多轴旋转策略，利用多模态信息实现鲁棒迁移；4）实现无微调的sim-to-real迁移，显著优于单模态或浅层融合方法。这些创新共同推动机器人多模态感知与操控技术的发展。
Methodology
- �� 在模拟环境中，使用Ground-truth的物理参数和形状信息，训练oracle策略，优化多轴旋转性能（采用PPO算法）。
- �� 构建多模态传感系统，包括深度相机、触觉传感器和本体感知，采集多模态数据。
- �� 利用PointNet编码器，将点云信息转化为特征向量，用于复杂形状理解。
- �� 设计Transformer模型，输入多模态时间序列数据，进行序列建模，推断隐含的物理属性和形状。
- �� 在模拟中训练多轴旋转任务，优化策略性能。
- �� 利用多模态传感器数据训练Transformer，估算privileged信息。
- �� 将训练好的模型迁移到真实环境，利用Segment-Anything进行目标分割，触觉通过视觉传感器检测接触点，融合多模态信息实现多轴旋转。
Experiments
采用多样化的物体数据集（EGAD、YCB、ContactDB等），在模拟和真实环境中进行评估。指标包括Time-to-Fall、旋转奖励和旋转惩罚。对比基线包括Hora方法和无形状信息模型，进行ablation研究。模拟中测试不同模态融合策略，验证点云编码和Transformer的贡献。真实环境中，评估多轴旋转成功率和泛化能力，特别是对out-of-distribution物体的表现。
Results
在模拟中，RotateIt在z轴旋转奖励达140.9，优于Hora（99.83）约40%。引入点云后，复杂形状物体的旋转奖励提升超过40%，泛化能力提升22%。在真实环境中，成功率提升20%以上，表现优异。多模态融合模型在OOD测试中，性能下降仅8-15%，优于单模态系统。模型还能准确预测3D形状，验证隐含特征的有效性。
Plain Language Accessible to non-experts
想象你在厨房做饭，手指就像厨师的手，拿着各种不同形状的食材。以前，厨师只能用眼睛看，或用手感觉，难以精准操作。现在，假如厨师戴上了“感知手套”，可以同时看到食材的3D轮廓，还能感觉到它们的硬度和温度。这样，厨师就能更快、更准地旋转和调整食材。RotateIt系统就像这个厨师，利用视觉和触觉信息，结合强大的学习算法，能在复杂环境中灵巧操作各种物体。它在模拟中学会技能，然后直接用到真实世界，无需重新调试，就能操作多样物体。这让机器人变得更聪明、更灵活，未来可以帮我们做很多事情，比如装配、搬运等。
ELI14 Explained like you're 14
你知道玩积木的时候，用眼睛看积木的形状，用手摸它的硬度和大小。以前，机器人只能用一种感官，比如只用眼睛或只用手，结果很难把积木旋转到正确位置。现在，有了RotateIt，就像给机器人装上了“超级感知眼镜”和“魔法手套”，它可以同时用眼睛和手感知积木的形状和硬度。这样，机器人就能更聪明地旋转积木，不管形状多复杂，都能轻松搞定。它在模拟环境中学会了这些技能，然后直接用到真实的机器人上，不需要重新调试。就像你学会了这个技巧后，能在任何积木游戏中都表现得很棒！这让机器人变得更聪明、更灵巧，将来可以帮我们做很多事情，比如装配、搬运、整理房间。
Abstract
We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill it to operate on realistic yet noisy simulated visuotactile and proprioceptive sensory inputs. These multimodal inputs are fused via a visuotactile transformer, enabling online inference of object shapes and physical properties during deployment. We show significant performance improvements over prior methods and the importance of visual and tactile sensing.