5 citations · 8 across the 12 of their papers we have counts for
16 papers
ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation
Pankhuri Vanjani, Mostafa Hatab, Can Mizrakli +4
Memory-dependent manipulation requires robots to make decisions using information that is no longer available to their current sensors, such as recalling an earlier visual cue, tra…
GeomVLA: Unifying Scene, Motion, and Action in 3D
Ziyin Xiong, Nikolaos Gkanatsios, Nikos Gkanatsios +2
We present GeomVLA, a Vision-Language-Action (VLA) model that unifies perception, latent scene motion prediction, and action generation within a shared robot-centric 3D coordinate…
DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation
Nisarga Nilavadi, Ralf Römer, Moritz Reuss +5
Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-gr…
SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale
Nils Blank, Paul Mattes, Maximilian Xiling Li +5
This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with…
DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model
Pankhuri Vanjani, Zhuoyue Li, Jakub Suliga +4
Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate. This is misaligned with physical intera…
NaviTrace: Evaluating Embodied Navigation of Vision-Language Models
Tim Windecker, Manthan Patel, Moritz Reuss +5
Vision-language models demonstrate unprecedented performance and generalization across a wide range of tasks and scenarios. Integrating these foundation models into robotic navigat…
PointMapPolicy: Structured Point Cloud Processing for Multi-Modal Imitation Learning
Xiaogang Jia, Qian Wang, Anrui Wang +12
Robotic manipulation systems benefit from complementary sensing modalities, where each provides unique environmental information. Point clouds capture detailed geometric structure,…
FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies
Moritz Reuss, Hongyi Zhou, Marcel Rühle +3
Developing efficient Vision-Language-Action (VLA) policies is crucial for practical robotics deployment, yet current approaches face prohibitive computational costs and resource re…
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning
Hongyi Zhou, Weiran Liao, Xi Huang +11
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…
Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning
Moritz Reuss, Jyothish Pari, Pulkit Agrawal +1
Diffusion Policies have become widely used in Imitation Learning, offering several appealing properties, such as generating multimodal and discontinuous behavior. As models are bec…
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models
Nils Blank, Moritz Reuss, Marcel Rühle +4
A central challenge towards developing robots that can relate human language to their perception and actions is the scarcity of natural language annotations in diverse robot datase…
Multimodal Diffusion Transformer: Learning Versatile Behavior from Multimodal Goals
Moritz Reuss, Ömer Erdinç Yağmurlu, Fabian Wenzel +1
This work introduces the Multimodal Diffusion Transformer (MDT), a novel diffusion policy framework, that excels at learning versatile behavior from multimodal goal specifications…
Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
Xiaogang Jia, Denis Blessing, Xinkai Jiang +4
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…
Goal-Conditioned Imitation Learning using Score-based Diffusion Policies
Moritz Reuss, Maximilian Li, Xiaogang Jia +1
We propose a new policy representation based on score-based diffusion models (SDMs). We apply our new policy representation in the domain of Goal-Conditioned Imitation Learning (GC…
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills
Denis Blessing, Onur Celik, Xiaogang Jia +4
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 distr…
End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
Moritz Reuss, Niels van Duijkeren, Robert Krug +3
It is well-known that inverse dynamics models can improve tracking performance in robot control. These models need to precisely capture the robot dynamics, which consist of well-un…