Author name cluster

Lukas Lao Beyer

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6 papers

1 author row

ICML Conference 2025 Conference Paper

Highly Compressed Tokenizer Can Generate Without Training

Lukas Lao Beyer
Tianhong Li
Xinlei Chen
Sertac Karaman
Kaiming He

Commonly used image tokenizers produce a 2D grid of spatially arranged tokens. In contrast, so-called 1D image tokenizers represent images as highly compressed one-dimensional sequences of as few as 32 discrete tokens. We find that the high degree of compression achieved by a 1D tokenizer with vector quantization enables image editing and generative capabilities through heuristic manipulation of tokens, demonstrating that even very crude manipulations – such as copying and replacing tokens between latent representations of images – enable fine-grained image editing by transferring appearance and semantic attributes. Motivated by the expressivity of the 1D tokenizer’s latent space, we construct an image generation pipeline leveraging gradient-based test-time optimization of tokens with plug-and-play loss functions such as reconstruction or CLIP similarity. Our approach is demonstrated for inpainting and text-guided image editing use cases, and can generate diverse and realistic samples without requiring training of any generative model.

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ICRA Conference 2025 Conference Paper

Joint Localization and Planning Using Diffusion

Lukas Lao Beyer
Sertac Karaman

Diffusion models have been successfully applied to robotics problems such as manipulation and vehicle path planning. In this work, we explore their application to end-to-end navigation - including both perception and planning - by considering the problem of jointly performing global localization and path planning in known but arbitrary 2D environments. In particular, we introduce a diffusion model which produces collision-free paths in a global reference frame given an egocentric LIDAR scan, an arbitrary map, and a desired goal position. To this end, we implement diffusion in the space of paths in $\text{SE}(2)$, and describe how to condition the denoising process on both obstacles and sensor observations. In our evaluation, we show that the proposed conditioning techniques enable generalization to realistic maps of considerably different appearance than the training environment, demonstrate our model's ability to accurately describe ambiguous solutions, and run extensive simulation experiments showcasing our model's use as a real-time, end-to-end localization and planning stack.

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ICRA Conference 2025 Conference Paper

Real-Time Sampling-based Online Planning for Drone Interception

Gilhyun Ryou
Lukas Lao Beyer
Sertac Karaman

This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational constraints for real-time adaptation, and accounting for uncertainty from environmental changes. To address these challenges, we propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization, enabling rapid exploration of multiple trajectory options under uncertainty. The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions. The algorithm efficiently generates trajectories toward multiple potential target drone positions in parallel. It then assesses trajectory reachability by comparing traversal times with the target drone's predicted arrival time, ultimately selecting the minimum-time reachable trajectory. Through extensive validation in both simulated and real-world environments, we demonstrate our method's capability for high-rate online planning and its adaptability to unpredictable movements in unstructured settings.

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IROS Conference 2024 Conference Paper

NVINS: Robust Visual Inertial Navigation Fused with NeRF-augmented Camera Pose Regressor and Uncertainty Quantification

Juyeop Han
Lukas Lao Beyer
Guilherme Venturelli Cavalheiro
Sertac Karaman

In recent years, Neural Radiance Fields (NeRF) have emerged as a powerful tool for 3D reconstruction and novel view synthesis. However, the computational cost of NeRF rendering and degradation in quality due to the presence of artifacts pose significant challenges for its application in real-time and robust robotic tasks, especially on embedded systems. This paper introduces a novel framework that integrates NeRF-derived localization information with Visual-Inertial Odometry (VIO) to provide a robust solution for real-time robotic navigation. By training an absolute pose regression network with augmented image data rendered from a NeRF and quantifying its uncertainty, our approach effectively counters positional drift and enhances system reliability. We also establish a mathematically sound foundation for combining visual inertial navigation with camera localization neural networks, considering uncertainty under a Bayesian framework. Experimental validation in a photorealistic simulation environment demonstrates significant improvements in accuracy compared to a conventional VIO approach.

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ICRA Conference 2024 Conference Paper

Risk-Predictive Planning for Off-Road Autonomy

Lukas Lao Beyer
Gilhyun Ryou
Patrick Spieler
Sertac Karaman

Efficiently navigating off-road environments presents a number of challenges arising from their unstructured nature. In the absence of high-fidelity maps, occlusions from obstacles and terrain lead to limited information available to inform planning decisions. Furthermore, resolution and latency limitations of real-world perception systems lead to potentially of degraded perception performance when traversing such environments at high speeds. We address these problems by proposing an algorithm which plans trajectories while anticipating future observations. In particular, we introduce a model which learns to predict the evolution of future riskmaps conditioned on the future path and speed profile of the vehicle. The model is trained in a self-supervised fashion using recordings of vehicle trajectories. We then present an algorithm which leverages a way to efficiently query the model along candidate paths and speed profiles to produce time-optimal trajectories while maintaining a bound on the future expected risk. We assess the predictive performance of our risk model through a comparison with real vehicle driving logs. Furthermore, our closed-loop simulations of several benchmark scenarios demonstrate how the behavior of our planner leads to qualitatively distinct trajectories, leading to improvements in both success rate and speed by up to 60%.

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ICRA Conference 2021 Conference Paper

Multi-Modal Motion Planning Using Composite Pose Graph Optimization

Lukas Lao Beyer
Nadya Balabanska
Ezra Tal
Sertac Karaman

In this paper, we present a motion planning framework for multi-modal vehicle dynamics. Our proposed algorithm employs transcription of the optimization objective function, vehicle dynamics, and state and control constraints into sparse factor graphs, which—combined with mode transition constraints—constitute a composite pose graph. By formulating the multi-modal motion planning problem in composite pose graph form, we enable utilization of efficient techniques for optimization on sparse graphs, such as those widely applied in dual estimation problems, e. g. , simultaneous localization and mapping (SLAM). The resulting motion planning algorithm optimizes the multi-modal trajectory, including the location of mode transitions, and is guided by the pose graph optimization process to eliminate unnecessary transitions, enabling efficient discovery of optimized mode sequences from rough initial guesses. We demonstrate multi-modal trajectory optimization in both simulation and real-world experiments for vehicles with various dynamics models, such as an airplane with taxi and flight modes, and a vertical take-off and landing (VTOL) fixed-wing aircraft that transitions between hover and horizontal flight modes.

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