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Fabian Otto

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7 papers
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7

NeurIPS Conference 2025 Conference Paper

BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning

  • Hongyi Zhou
  • Weiran Liao
  • Xi Huang
  • Yucheng Tang
  • Fabian Otto
  • Xiaogang Jia
  • Xinkai Jiang
  • Simon Hilber

We present the B-spline Encoded Action Sequence Tokenizer (BEAST), a novel action tokenizer that encodes action sequences into compact discrete or continuous tokens using B-splines. In contrast to existing action tokenizers based on vector quantization or byte pair encoding, BEAST requires no separate tokenizer training and consistently produces tokens of uniform length, enabling fast action sequence generation via parallel decoding. Leveraging our B-spline formulation, BEAST inherently ensures generating smooth trajectories without discontinuities between adjacent segments. We extensively evaluate BEAST by integrating it with three distinct model architectures: a Variational Autoencoder (VAE) with continuous tokens, a decoder-only Transformer with discrete tokens, and Florence-2, a pretrained Vision-Language Model with an encoder-decoder architecture, demonstrating BEAST's compatibility and scalability with large pretrained models. We evaluate BEAST across three established benchmarks consisting of 166 simulated tasks and on three distinct robot settings with a total of 8 real-world tasks. Experimental results demonstrate that BEAST (i) significantly reduces both training and inference computational costs, and (ii) consistently generates smooth, high-frequency control signals suitable for continuous control tasks while (iii) reliably achieves competitive task success rates compared to state-of-the-art methods.

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ICLR Conference 2025 Conference Paper

Efficient Off-Policy Learning for High-Dimensional Action Spaces

  • Fabian Otto
  • Philipp Becker
  • Ngo Anh Vien
  • Gerhard Neumann

Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spaces due to the curse of dimensionality. This reliance results in data inefficiency as maintaining a state-action-value function in such spaces is challenging. We present an efficient approach that utilizes only a state-value function as the critic for off-policy deep reinforcement learning. This approach, which we refer to as Vlearn, effectively circumvents the limitations of existing methods by eliminating the necessity for an explicit state-action-value function. To this end, we leverage a weighted importance sampling loss for learning deep value functions from off-policy data. While this is common for linear methods, it has not been combined with deep value function networks. This transfer to deep methods is not straightforward and requires novel design choices such as robust policy updates, twin value function networks to avoid an optimization bias, and importance weight clipping. We also present a novel analysis of the variance of our estimate compared to commonly used importance sampling estimators such as V-trace. Our approach improves sample complexity as well as final performance and ensures consistent and robust performance across various benchmark tasks. Eliminating the state-action-value function in Vlearn facilitates a streamlined learning process, yielding high-return agents.

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ICRA Conference 2025 Conference Paper

Robust Optical Transceiver Manipulation in Cluttered Cable Environments Using 3D Scene Understanding and Planning

  • Iason Sarantopoulos
  • Chenyu Liu
  • Bohong Weng
  • Sicheng Xu
  • Yizhong Zhang
  • Jiaolong Yang
  • Xin Tong 0001
  • Fabian Otto

Robotic manipulation in cluttered environments presents significant challenges, particularly when the clutter includes thin, deformable objects like cables, which complicate perception and decision-making processes. In the context of datacenters, the automation of networking tasks often involves the manipulation of optical transceivers within densely packed cable configurations. Such environments are characterized by an abundance of delicate, overlapping, and intersecting cables, leading to frequent occlusions. This paper introduces an innovative system designed for the manipulation of optical transceivers in environments cluttered by cables. Our integrated approach combines advanced 3D scene understanding with a heuristic-based pushing policy to effectively manipulate optical transceivers amidst clutter. The system's perception component utilizes image segmentation and 3D reconstruction to accurately model the transceivers and surrounding cables. Meanwhile, the planning aspect employs a search algorithm with task-specific heuristics, to navigate the gripper, displace obstructing cables, and safely achieve a precise pre-grasp position in front of the target transceiver. We have conducted extensive evaluations of our methodology in both simulated and real-world settings, demonstrating its high success rates, robustness, and proficiency in addressing the unique challenges posed by cable-occluded environments within datacenters.

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RLC Conference 2024 Conference Paper

Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL

  • Philipp Becker
  • Sebastian Mossburger
  • Fabian Otto
  • Gerhard Neumann

Learning self-supervised representations using reconstruction or contrastive losses improves performance and sample complexity of image-based and multimodal reinforcement learning (RL). Here, different self-supervised loss functions have distinct advantages and limitations depending on the information density of the underlying sensor modality. Reconstruction provides strong learning signals but is susceptible to distractions and spurious information. While contrastive approaches can ignore those, they may fail to capture all relevant details and can lead to representation collapse. For multimodal RL, this suggests that different modalities should be treated differently, based on the amount of distractions in the signal. We propose Contrastive Reconstructive Aggregated representation Learning (CoRAL), a unified framework enabling us to choose the most appropriate self-supervised loss for each sensor modality and allowing the representation to better focus on relevant aspects. We evaluate CoRAL's benefits on a wide range of tasks with images containing distractions or occlusions, a new locomotion suite, and a challenging manipulation suite with visually realistic distractions. Our results show that learning a multimodal representation by combining contrastive and reconstruction-based losses can significantly improve performance and allow for solving tasks that are out of reach for more naive representation learning approaches and other recent baselines.

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RLJ Journal 2024 Journal Article

Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL

  • Philipp Becker
  • Sebastian Mossburger
  • Fabian Otto
  • Gerhard Neumann

Learning self-supervised representations using reconstruction or contrastive losses improves performance and sample complexity of image-based and multimodal reinforcement learning (RL). Here, different self-supervised loss functions have distinct advantages and limitations depending on the information density of the underlying sensor modality. Reconstruction provides strong learning signals but is susceptible to distractions and spurious information. While contrastive approaches can ignore those, they may fail to capture all relevant details and can lead to representation collapse. For multimodal RL, this suggests that different modalities should be treated differently, based on the amount of distractions in the signal. We propose Contrastive Reconstructive Aggregated representation Learning (CoRAL), a unified framework enabling us to choose the most appropriate self-supervised loss for each sensor modality and allowing the representation to better focus on relevant aspects. We evaluate CoRAL's benefits on a wide range of tasks with images containing distractions or occlusions, a new locomotion suite, and a challenging manipulation suite with visually realistic distractions. Our results show that learning a multimodal representation by combining contrastive and reconstruction-based losses can significantly improve performance and allow for solving tasks that are out of reach for more naive representation learning approaches and other recent baselines.

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ICLR Conference 2024 Conference Paper

Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning

  • Ge Li
  • Hongyi Zhou
  • Dominik Roth
  • Serge Thilges
  • Fabian Otto
  • Rudolf Lioutikov
  • Gerhard Neumann

Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to implement on real hardware. Episodic RL (ERL) seeks to overcome these challenges by exploring in parameters space that capture the correlation of actions. However, these approaches typically compromise data efficiency, as they treat trajectories as opaque black boxes. In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL (TCE), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space. TCE synergistically combines the advantages of step-based and episodic RL, achieving comparable performance to recent ERL methods while maintaining data efficiency akin to state-of-the-art (SoTA) step-based RL.

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ICLR Conference 2021 Conference Paper

Differentiable Trust Region Layers for Deep Reinforcement Learning

  • Fabian Otto
  • Philipp Becker
  • Ngo Anh Vien
  • Hanna Ziesche
  • Gerhard Neumann

Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), are based on approximations. Due to those approximations, they violate the constraints or fail to find the optimal solution within the trust region. Moreover, they are difficult to implement, often lack sufficient exploration, and have been shown to depend on seemingly unrelated implementation choices. In this work, we propose differentiable neural network layers to enforce trust regions for deep Gaussian policies via closed-form projections. Unlike existing methods, those layers formalize trust regions for each state individually and can complement existing reinforcement learning algorithms. We derive trust region projections based on the Kullback-Leibler divergence, the Wasserstein L2 distance, and the Frobenius norm for Gaussian distributions. We empirically demonstrate that those projection layers achieve similar or better results than existing methods while being almost agnostic to specific implementation choices. The code is available at https://git.io/Jthb0.

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