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  1. Book ; Online: A Wearable Robotic Hand for Hand-over-Hand Imitation Learning

    Wei, Dehao / Xu, Huazhe

    2023  

    Abstract: Dexterous manipulation through imitation learning has gained significant attention in robotics research. The collection of high-quality expert data holds paramount importance when using imitation learning. The existing approaches for acquiring expert ... ...

    Abstract Dexterous manipulation through imitation learning has gained significant attention in robotics research. The collection of high-quality expert data holds paramount importance when using imitation learning. The existing approaches for acquiring expert data commonly involve utilizing a data glove to capture hand motion information. However, this method suffers from limitations as the collected information cannot be directly mapped to the robotic hand due to discrepancies in their degrees of freedom or structures. Furthermore,it fails to accurately capture force feedback information between the hand and objects during the demonstration process. To overcome these challenges, this paper presents a novel solution in the form of a wearable dexterous hand, namely Hand-over-hand Imitation learning wearable RObotic Hand (HIRO Hand),which integrates expert data collection and enables the implementation of dexterous operations. This HIRO Hand empowers the operator to utilize their own tactile feedback to determine appropriate force, position, and actions, resulting in more accurate imitation of the expert's actions. We develop both non-learning and visual behavior cloning based controllers allowing HIRO Hand successfully achieves grasping and in-hand manipulation ability.

    Comment: 7 pages
    Keywords Computer Science - Robotics
    Subject code 004
    Publishing date 2023-09-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: DittoGym

    Huang, Suning / Chen, Boyuan / Xu, Huazhe / Sitzmann, Vincent

    Learning to Control Soft Shape-Shifting Robots

    2024  

    Abstract: Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that ... ...

    Abstract Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that can realize learned morphologies and actuators. Inspired by nature and recent novel robot designs, we propose to go a step further and explore the novel reconfigurable robots, defined as robots that can change their morphology within their lifetime. We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem. We unify morphology change, locomotion, and environment interaction in the same action space, and introduce an appropriate, coarse-to-fine curriculum that enables us to discover policies that accomplish fine-grained control of the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark for reconfigurable soft robots that require fine-grained morphology changes to accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine algorithm on DittoGym and demonstrate robots that learn to change their morphology several times within a sequence, uniquely enabled by our RL algorithm. More results are available at https://dittogym.github.io.
    Keywords Computer Science - Robotics ; Computer Science - Machine Learning
    Subject code 629
    Publishing date 2024-01-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function

    Zheng, Ruijie / Wang, Xiyao / Xu, Huazhe / Huang, Furong

    2023  

    Abstract: Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance and sample efficiency. In this paper, we provide both practical and ... ...

    Abstract Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance and sample efficiency. In this paper, we provide both practical and theoretical insights on the empirical success of the probabilistic dynamics model ensemble through the lens of Lipschitz continuity. We find that, for a value function, the stronger the Lipschitz condition is, the smaller the gap between the true dynamics- and learned dynamics-induced Bellman operators is, thus enabling the converged value function to be closer to the optimal value function. Hence, we hypothesize that the key functionality of the probabilistic dynamics model ensemble is to regularize the Lipschitz condition of the value function using generated samples. To test this hypothesis, we devise two practical robust training mechanisms through computing the adversarial noise and regularizing the value network's spectral norm to directly regularize the Lipschitz condition of the value functions. Empirical results show that combined with our mechanisms, model-based RL algorithms with a single dynamics model outperform those with an ensemble of probabilistic dynamics models. These findings not only support the theoretical insight, but also provide a practical solution for developing computationally efficient model-based RL algorithms.

    Comment: ICLR 2023
    Keywords Computer Science - Machine Learning
    Subject code 612
    Publishing date 2023-02-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: MoVie

    Yang, Sizhe / Ze, Yanjie / Xu, Huazhe

    Visual Model-Based Policy Adaptation for View Generalization

    2023  

    Abstract: Visual Reinforcement Learning (RL) agents trained on limited views face significant challenges in generalizing their learned abilities to unseen views. This inherent difficulty is known as the problem of $\textit{view generalization}$. In this work, we ... ...

    Abstract Visual Reinforcement Learning (RL) agents trained on limited views face significant challenges in generalizing their learned abilities to unseen views. This inherent difficulty is known as the problem of $\textit{view generalization}$. In this work, we systematically categorize this fundamental problem into four distinct and highly challenging scenarios that closely resemble real-world situations. Subsequently, we propose a straightforward yet effective approach to enable successful adaptation of visual $\textbf{Mo}$del-based policies for $\textbf{Vie}$w generalization ($\textbf{MoVie}$) during test time, without any need for explicit reward signals and any modification during training time. Our method demonstrates substantial advancements across all four scenarios encompassing a total of $\textbf{18}$ tasks sourced from DMControl, xArm, and Adroit, with a relative improvement of $\mathbf{33}$%, $\mathbf{86}$%, and $\mathbf{152}$% respectively. The superior results highlight the immense potential of our approach for real-world robotics applications. Videos are available at https://yangsizhe.github.io/MoVie/ .

    Comment: Accepted in NeurIPS 2023. The first two authors contribute equally
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Robotics
    Subject code 006
    Publishing date 2023-07-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Diffusion Reward

    Huang, Tao / Jiang, Guangqi / Ze, Yanjie / Xu, Huazhe

    Learning Rewards via Conditional Video Diffusion

    2023  

    Abstract: Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning tasks. In this work, we propose Diffusion Reward, a novel framework that learns rewards from expert videos via ... ...

    Abstract Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning tasks. In this work, we propose Diffusion Reward, a novel framework that learns rewards from expert videos via conditional video diffusion models for solving complex visual RL problems. Our key insight is that lower generative diversity is observed when conditioned on expert trajectories. Diffusion Reward is accordingly formalized by the negative of conditional entropy that encourages productive exploration of expert-like behaviors. We show the efficacy of our method over 10 robotic manipulation tasks from MetaWorld and Adroit with visual input and sparse reward. Moreover, Diffusion Reward could even solve unseen tasks successfully and effectively, largely surpassing baseline methods. Project page and code: https://diffusion-reward.github.io/.

    Comment: Project page and code: https://diffusion-reward.github.io/
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Robotics
    Subject code 004
    Publishing date 2023-12-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  6. Book ; Online: Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning?

    Gao, Jialu / Hu, Kaizhe / Xu, Guowei / Xu, Huazhe

    2023  

    Abstract: Pre-trained text-to-image generative models can produce diverse, semantically rich, and realistic images from natural language descriptions. Compared with language, images usually convey information with more details and less ambiguity. In this study, we ...

    Abstract Pre-trained text-to-image generative models can produce diverse, semantically rich, and realistic images from natural language descriptions. Compared with language, images usually convey information with more details and less ambiguity. In this study, we propose Learning from the Void (LfVoid), a method that leverages the power of pre-trained text-to-image models and advanced image editing techniques to guide robot learning. Given natural language instructions, LfVoid can edit the original observations to obtain goal images, such as "wiping" a stain off a table. Subsequently, LfVoid trains an ensembled goal discriminator on the generated image to provide reward signals for a reinforcement learning agent, guiding it to achieve the goal. The ability of LfVoid to learn with zero in-domain training on expert demonstrations or true goal observations (the void) is attributed to the utilization of knowledge from web-scale generative models. We evaluate LfVoid across three simulated tasks and validate its feasibility in the corresponding real-world scenarios. In addition, we offer insights into the key considerations for the effective integration of visual generative models into robot learning workflows. We posit that our work represents an initial step towards the broader application of pre-trained visual generative models in the robotics field. Our project page: https://lfvoid-rl.github.io/.
    Keywords Computer Science - Robotics
    Subject code 004
    Publishing date 2023-07-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Robo-ABC

    Ju, Yuanchen / Hu, Kaizhe / Zhang, Guowei / Zhang, Gu / Jiang, Mingrun / Xu, Huazhe

    Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation

    2024  

    Abstract: Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step toward open-world embodied intelligence. For human beings, this ability is rooted in the understanding of semantic correspondence among objects, which ... ...

    Abstract Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step toward open-world embodied intelligence. For human beings, this ability is rooted in the understanding of semantic correspondence among objects, which naturally transfers the interaction experience of familiar objects to novel ones. Although robots lack such a reservoir of interaction experience, the vast availability of human videos on the Internet may serve as a valuable resource, from which we extract an affordance memory including the contact points. Inspired by the natural way humans think, we propose Robo-ABC: when confronted with unfamiliar objects that require generalization, the robot can acquire affordance by retrieving objects that share visual or semantic similarities from the affordance memory. The next step is to map the contact points of the retrieved objects to the new object. While establishing this correspondence may present formidable challenges at first glance, recent research finds it naturally arises from pre-trained diffusion models, enabling affordance mapping even across disparate object categories. Through the Robo-ABC framework, robots may generalize to manipulate out-of-category objects in a zero-shot manner without any manual annotation, additional training, part segmentation, pre-coded knowledge, or viewpoint restrictions. Quantitatively, Robo-ABC significantly enhances the accuracy of visual affordance retrieval by a large margin of 31.6% compared to state-of-the-art (SOTA) end-to-end affordance models. We also conduct real-world experiments of cross-category object-grasping tasks. Robo-ABC achieved a success rate of 85.7%, proving its capacity for real-world tasks.
    Keywords Computer Science - Robotics ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2024-01-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Decision Transformer under Random Frame Dropping

    Hu, Kaizhe / Zheng, Ray Chen / Gao, Yang / Xu, Huazhe

    2023  

    Abstract: Controlling agents remotely with deep reinforcement learning~(DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the face of dropped information from corrupted communication or malfunctioning ... ...

    Abstract Controlling agents remotely with deep reinforcement learning~(DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the face of dropped information from corrupted communication or malfunctioning sensors. Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world. Furthermore, when applying to the frame dropping scenarios, they perform unsatisfactorily even with moderate drop rates. To address these issues, we propose Decision Transformer under Random Frame Dropping~(DeFog), an offline RL algorithm that enables agents to act robustly in frame dropping scenarios without online interaction. DeFog first randomly masks out data in the offline datasets and explicitly adds the time span of frame dropping as inputs. After that, a finetuning stage on the same offline dataset with a higher mask rate would further boost the performance. Empirical results show that DeFog outperforms strong baselines under severe frame drop rates like 90\%, while maintaining similar returns under non-frame-dropping conditions in the regular MuJoCo control benchmarks and the Atari environments. Our approach offers a robust and deployable solution for controlling agents in real-world environments with limited or unreliable data.

    Comment: 20 pages, 17 figures; accepted to ICLR 2023
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Robotics
    Subject code 006
    Publishing date 2023-03-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: RoboCook

    Shi, Haochen / Xu, Huazhe / Clarke, Samuel / Li, Yunzhu / Wu, Jiajun

    Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools

    2023  

    Abstract: Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark of human cognition, tool use in autonomous robots ... ...

    Abstract Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark of human cognition, tool use in autonomous robots remains limited due to challenges in understanding tool-object interactions. Here we develop an intelligent robotic system, RoboCook, which perceives, models, and manipulates elasto-plastic objects with various tools. RoboCook uses point cloud scene representations, models tool-object interactions with Graph Neural Networks (GNNs), and combines tool classification with self-supervised policy learning to devise manipulation plans. We demonstrate that from just 20 minutes of real-world interaction data per tool, a general-purpose robot arm can learn complex long-horizon soft object manipulation tasks, such as making dumplings and alphabet letter cookies. Extensive evaluations show that RoboCook substantially outperforms state-of-the-art approaches, exhibits robustness against severe external disturbances, and demonstrates adaptability to different materials.

    Comment: Project page: https://hshi74.github.io/robocook/
    Keywords Computer Science - Robotics
    Subject code 629
    Publishing date 2023-06-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: 9DTact

    Lin, Changyi / Zhang, Han / Xu, Jikai / Wu, Lei / Xu, Huazhe

    A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation

    2023  

    Abstract: The advancements in vision-based tactile sensors have boosted the aptitude of robots to perform contact-rich manipulation, particularly when precise positioning and contact state of the manipulated objects are crucial for successful execution. In this ... ...

    Abstract The advancements in vision-based tactile sensors have boosted the aptitude of robots to perform contact-rich manipulation, particularly when precise positioning and contact state of the manipulated objects are crucial for successful execution. In this work, we present 9DTact, a straightforward yet versatile tactile sensor that offers 3D shape reconstruction and 6D force estimation capabilities. Conceptually, 9DTact is designed to be highly compact, robust, and adaptable to various robotic platforms. Moreover, it is low-cost and easy-to-fabricate, requiring minimal assembly skills. Functionally, 9DTact builds upon the optical principles of DTact and is optimized to achieve 3D shape reconstruction with enhanced accuracy and efficiency. Remarkably, we leverage the optical and deformable properties of the translucent gel so that 9DTact can perform 6D force estimation without the participation of auxiliary markers or patterns on the gel surface. More specifically, we collect a dataset consisting of approximately 100,000 image-force pairs from 175 complex objects and train a neural network to regress the 6D force, which can generalize to unseen objects. To promote the development and applications of vision-based tactile sensors, we open-source both the hardware and software of 9DTact, along with a comprehensive video tutorial, all of which are available at https://linchangyi1.github.io/9DTact.

    Comment: Project Website: https://linchangyi1.github.io/9DTact/
    Keywords Computer Science - Robotics
    Subject code 004
    Publishing date 2023-08-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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