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Diffusion for Multi-Embodiment Grasping

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2694-2701页

作者： Freiberg, Roman Qualmann, Alexander Vien, Ngo Anh Neumann, Gerhard BOSCH Corp Res D-71272 Renningen Germany Karlsruhe Inst Technol Autonomous Learning Robots Lab D-76137 Karlsruhe Germany

Grasping is a fundamental skill in robotics with diverse applications. However, current approaches for predicting successful grasps are often tailored to specific grippers, limiting their applicability when gripper designs change. To address this limitation, we explore the transfer of grasping strategies between various gripper designs, enabling the use of data from diverse sources. We present an approach based on equivariant diffusion that facilitates gripper-agnostic encoding of scenes containing graspable objects and gripper-aware decoding of grasp poses by integrating gripper geometry into the model. We also develop a dataset generation framework that produces cluttered scenes with variable-sized object heaps, improving the training of grasp synthesis methods. Experimental evaluation on diverse object datasets demonstrates the generalizability of our approach across gripper architectures, ranging from simple parallel-jaw grippers to humanoid hands, outperforming both single-gripper and multi-gripper State-of-the-Art methods.

关键词： Grippers Grasping robots Point cloud compression Manifolds Hands Training Robot vision systems Pipelines Internet deep learning in grasping and manipulation transfer learning

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MaIL: Improving Imitation learning with Selective State Space Models 8

MaIL: Improving Imitation Learning with Selective State Spac...

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8th Conference on Robot learning, CoRL 2024

作者： Jia, Xiaogang Wang, Qian Donat, Atalay Xing, Bowen Li, Ge Zhou, Hongyi Celik, Onur Blessing, Denis Lioutikov, Rudolf Neumann, Gerhard Autonomous Learning Robots Karlsruhe Institute of Technology Germany Intuitive Robots Lab Karlsruhe Institute of Technology Germany

This work presents Mamba Imitation learning (MaIL), a novel imitation learning (IL) architecture that provides an alternative to state-of-the-art (SoTA) Transformer-based policies. MaIL leverages Mamba, a state-space model designed to selectively focus on key features of the data. While Transformers are highly effective in data-rich environments due to their dense attention mechanisms, they can struggle with smaller datasets, often leading to overfitting or suboptimal representation learning. In contrast, Mamba's architecture enhances representation learning efficiency by focusing on key features and reducing model complexity. This approach mitigates overfitting and enhances generalization, even when working with limited data. Extensive evaluations on the LIBERO benchmark demonstrate that MaIL consistently outperforms Transformers on all LIBERO tasks with limited data and matches their performance when the full dataset is available. Additionally, MaIL's effectiveness is validated through its superior performance in three real robot experiments. Our code is available at https://***/ALRhub/MaIL. © 2024 Proceedings of Machine learning Research.

关键词： Contrastive learning

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TOWARDS DIVERSE BEHAVIORS: A BENCHMARK FOR IMITATION learning WITH HUMAN DEMONSTRATIONS 12

TOWARDS DIVERSE BEHAVIORS: A BENCHMARK FOR IMITATION LEARNIN...

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12th International Conference on learning Representations, ICLR 2024

作者： Jia, Xiaogang Blessing, Denis Jiang, Xinkai Reuss, Moritz Donat, Atalay Lioutikov, Rudolf Neumann, Gerhard Autonomous Learning Robots Karlsruhe Institute of Technology Germany Intuitive Robots Lab Karlsruhe Institute of Technology Germany

Imitation learning with human data has demonstrated remarkable success in teaching robots in a wide range of skills. However, the inherent diversity in human behavior leads to the emergence of multi-modal data distributions, thereby presenting a formidable challenge for existing imitation learning algorithms. Quantifying a model's capacity to capture and replicate this diversity effectively is still an open problem. In this work, we introduce simulation benchmark environments and the corresponding Datasets with Diverse human Demonstrations for Imitation learning (D3IL), designed explicitly to evaluate a model's ability to learn multi-modal behavior. Our environments are designed to involve multiple sub-tasks that need to be solved, consider manipulation of multiple objects which increases the diversity of the behavior and can only be solved by policies that rely on closed loop sensory feedback. Other available datasets are missing at least one of these challenging properties. To address the challenge of diversity quantification, we introduce tractable metrics that provide valuable insights into a model's ability to acquire and reproduce diverse behaviors. These metrics offer a practical means to assess the robustness and versatility of imitation learning algorithms. Furthermore, we conduct a thorough evaluation of state-of-the-art methods on the proposed task suite. This evaluation serves as a benchmark for assessing their capability to learn diverse behaviors. Our findings shed light on the effectiveness of these methods in tackling the intricate problem of capturing and generalizing multi-modal human behaviors, offering a valuable reference for the design of future imitation learning algorithms. Project page: https://***/d3il-website/. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： learning algorithms

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Curriculum-Based Imitation of Versatile Skills

Curriculum-Based Imitation of Versatile Skills

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Li, Maximilian Xiling Celik, Onur Becker, Philipp Blessing, Denis Lioutikov, Rudolf Neumann, Gerhard Karlsruhe Inst Technol Intuit Robots Lab Karlsruhe Germany Karlsruhe Inst Technol Autonomous Learning Robots Karlsruhe Germany

ISBN: (纸本)9798350323658

learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning methods that are based on such a maximum likelihood (ML) objective. The ML objective forces the model to cover all data, it prevents specialization in the context space and can cause mode-averaging in the behavior space, leading to suboptimal or potentially catastrophic behavior. Here, we alleviate those issues by introducing a curriculum using a weight for each data point, allowing the model to specialize on data it can represent while incentivizing it to cover as much data as possible by an entropy bonus. We extend our algorithm to a Mixture of (linear) Experts (MoE) such that the single components can specialize on local context regions, while the MoE covers all data points. We evaluate our approach in complex simulated and real robot control tasks and show it learns from versatile human demonstrations and significantly outperforms current SOTA methods. (1)

关键词： Imitation Versatility Curriculum learning

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Variational Distillation of Diffusion Policies into Mixture of Experts 38

Variational Distillation of Diffusion Policies into Mixture ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Hongyi Blessing, Denis Li, Ge Celik, Onur Jia, Xiaogang Neumann, Gerhard Lioutikov, Rudolf Intuitive Robots Lab Karlsruhe Institute of Technology Germany Autonomous Learning Robots Karlsruhe Institute of Technology Germany FZI Research Center for Information Technology Germany

This work introduces Variational Diffusion Distillation (VDD), a novel method that distills denoising diffusion policies into Mixtures of Experts (MoE) through variational inference. Diffusion Models are the current state-of-the-art in generative modeling due to their exceptional ability to accurately learn and represent complex, multi-modal distributions. This ability allows Diffusion Models to replicate the inherent diversity in human behavior, making them the preferred models in behavior learning such as learning from Human Demonstrations (LfD). However, diffusion models come with some drawbacks, including the intractability of likelihoods and long inference times due to their iterative sampling process. The inference times, in particular, pose a significant challenge to real-time applications such as robot control. In contrast, MoEs effectively address the aforementioned issues while retaining the ability to represent complex distributions but are notoriously difficult to train. VDD is the first method that distills pre-trained diffusion models into MoE models, and hence, combines the expressiveness of Diffusion Models with the benefits of Mixture Models. Specifically, VDD leverages a decompositional upper bound of the variational objective that allows the training of each expert separately, resulting in a robust optimization scheme for MoEs. VDD demonstrates across nine complex behavior learning tasks, that it is able to: i) accurately distill complex distributions learned by the diffusion model, ii) outperform existing state-of-the-art distillation methods, and iii) surpass conventional methods for training MoE. The code and videos are available at https://***/vdd-website. © 2024 Neural information processing systems foundation. All rights reserved.

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Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming 15th

Domain-Specific Fine-Tuning of Large Language Models for Int...

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15th European Robotics Forum

作者： Alt, Benjamin Kellner, Urs Taranovic, Aleksandar Katic, Darko Hermann, Andreas Jaekle, Rainer Neumann, Gerhard ArtiMinds Robot D-76131 Karlsruhe Germany Karlsruhe Inst Technol Autonomous Learning Robots Lab D-76131 Karlsruhe Germany

ISBN: (纸本)9783031764264;9783031764240;9783031764233

Industrial robots are applied in a widening range of industries, but robot programming mostly remains a task limited to programming experts. We propose a natural language-based assistant for programming of advanced, industrial robotic applications and investigate strategies for domain-specific fine-tuning of foundation models with limited data and compute.

关键词： large language models robot programming fine-tuning

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Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills 37

Information Maximizing Curriculum: A Curriculum-Based Approa...

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37th Conference on Neural Information Processing Systems (NeurIPS)

作者： Blessing, Denis Celik, Onur Jia, Xiaogang Reuss, Moritz Li, Maximilian Xiling Lioutikov, Rudolf Neumann, Gerhard Karlsruhe Inst Technol Autonomous Learning Robots Karlsruhe Germany Karlsruhe Inst Technol Intuit Robots Lab Karlsruhe Germany FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9781713899921

Imitation learning uses data for training policies to solve complex tasks. However, when the training data is collected from human demonstrators, it often leads to multimodal distributions because of the variability in human actions. Most imitation learning methods rely on a maximum likelihood (ML) objective to learn a parameterized policy, but this can result in suboptimal or unsafe behavior due to the mode-averaging property of the ML objective. In this work, we propose Information Maximizing Curriculum, a curriculum-based approach that assigns a weight to each data point and encourages the model to specialize in the data it can represent, effectively mitigating the mode-averaging problem by allowing the model to ignore data from modes it cannot represent. To cover all modes and thus, enable diverse behavior, we extend our approach to a mixture of experts (MoE) policy, where each mixture component selects its own subset of the training data for learning. A novel, maximum entropy-based objective is proposed to achieve full coverage of the dataset, thereby enabling the policy to encompass all modes within the data distribution. We demonstrate the effectiveness of our approach on complex simulated control tasks using diverse human demonstrations, achieving superior performance compared to state-of-the-art methods.

关键词： Demonstrations

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ADVERSARIAL IMITATION learning WITH PREFERENCES 11

ADVERSARIAL IMITATION LEARNING WITH PREFERENCES

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11th International Conference on learning Representations, ICLR 2023

作者： Taranovic, Aleksandar Kupcsik, Andras Freymuth, Niklas Neumann, Gerhard Autonomous Learning Robots Lab Karlsruhe Institute of Technology Karlsruhe Germany Bosch Center for Artificial Intelligence Renningen Germany

Designing an accurate and explainable reward function for many Reinforcement learning tasks is a cumbersome and tedious process. Instead, learning policies directly from the feedback of human teachers naturally integrates human domain knowledge into the policy optimization process. Different feedback modalities, such as demonstrations and preferences, provide distinct benefits and disadvantages. For example, demonstrations convey a lot of information about the task but are often hard or costly to obtain from real experts while preferences typically contain less information but are in most cases cheap to generate. However, existing methods centered around human feedback mostly focus on a single teaching modality, causing them to miss out on important training data while making them less intuitive to use. In this paper we propose a novel method for policy learning that incorporates two different feedback types, namely demonstrations and preferences. To this end, we make use of the connection between discriminator training and density ratio estimation to incorporate preferences into the popular Adversarial Imitation learning paradigm. This insight allows us to express loss functions over both demonstrations and preferences in a unified framework. Besides expert demonstrations, we are also able to learn from imperfect ones and combine them with preferences to achieve improved task performance. We experimentally validate the effectiveness of combining both preferences and demonstrations on common benchmarks and also show that our method can efficiently learn challenging robot manipulation tasks. © 2023 11th International Conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Demonstrations

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Variational distillation of diffusion policies into mixture of experts 24

Variational distillation of diffusion policies into mixture ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hongyi Zhou Denis Blessing Ge Li Onur Celik Xiaogang Jia Gerhard Neumann Rudolf Lioutikov Intuitive Robots Lab Karlsruhe Institute of Technology Autonomous Learning Robots Karlsruhe Institute of Technology Autonomous Learning Robots Karlsruhe Institute of Technology and FZI Research Center for Information Technology Intuitive Robots Lab Karlsruhe Institute of Technology and Autonomous Learning Robots Karlsruhe Institute of Technology

ISBN: (纸本)9798331314385

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MaIL: Improving Imitation learning with Mamba

arXiv

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arXiv 2024年

This work presents Mamba Imitation learning (MaIL), a novel imitation learning (IL) architecture that provides an alternative to state-of-the-art (SoTA) Transformer-based policies. MaIL leverages Mamba, a state-space model designed to selectively focus on key features of the data. While Transformers are highly effective in data-rich environments due to their dense attention mechanisms, they can struggle with smaller datasets, often leading to overfitting or suboptimal representation learning. In contrast, Mamba’s architecture enhances representation learning efficiency by focusing on key features and reducing model complexity. This approach mitigates overfitting and enhances generalization, even when working with limited data. Extensive evaluations on the LIBERO benchmark demonstrate that MaIL consistently outperforms Transformers on all LIBERO tasks with limited data and matches their performance when the full dataset is available. Additionally, MaIL’s effectiveness is validated through its superior performance in three real robot experiments. Our code is available at https://***/ALRhub/MaIL. © 2024, CC BY.

关键词： Contrastive learning

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