I am a third year PhD student in the Autonomous Learning Robots (ALR) Lab at the
Karlsruhe Institute of Technology (KIT), supervised by Gerhard Neumann.
My research focuses on variational inference and sampling methods, with a focus on diffusion models.
Prior to this, I completed my Master's Degree in Computer Science at KIT, where my thesis focused on imitation learning of diverse skills.
Contact:denis.blessing@kit.edu
List of selected publications (* denotes equal contribution)
2025
Preprint
Learning Boltzmann Generators via Constrained Mass Transport
Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in sampling from intractable probability distributions. However, current studies lack a unified evaluation framework, relying on disparate performance measures and limited method comparisons across diverse tasks, complicating the assessment of progress and hindering the decision-making of practitioners. In response to these challenges, our work introduces a benchmark that evaluates sampling methods using a standardized task suite and a broad range of performance criteria. Moreover, we study existing metrics for quantifying mode collapse and introduce novel metrics for this purpose. Our findings provide insights into strengths and weaknesses of existing sampling methods, serving as a valuable reference for future developments. The code is publicly available here.
ICLR
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of the \emph{Controlled Monte Carlo Diffusion} sampler (CMCD) for Bayesian computation, a score-based annealing technique that crucially adapts both forward and backward dynamics in a diffusion model. On the way, we clarify the relationship between the EM-algorithm and iterative proportional fitting (IPF) for Schr{ö}dinger bridges, deriving as well a regularised objective that bypasses the iterative bottleneck of standard IPF-updates. Finally, we show that CMCD has a strong foundation in the Jarzinsky and Crooks identities from statistical physics, and that it convincingly outperforms competing approaches across a wide array of experiments.
ICLR
Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
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.
2023
NeurIPS
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills
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.
