103 citations · 328 across the 79 of their papers we have counts for
20 papers · 2 filters
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies
Sajad Marvi, Julian Schmidt, Alessandro Canevaro +2
Learning robust driving policies that generalize beyond their training domain remains a central challenge in motion planning. We propose an uncertainty-driven self-play scheme, in…
Distillation for Efficient Multitask Manipulation Policies via Conditional Flow Matching
Shreya Deshmukh, Imen Mahdi, Nick Heppert +1
Advances in generative modeling have recently been extensively employed in robotics for policy learning. In particular, Conditional Flow Matching (CFM) trained with expert demonstr…
CounterPlay: Counterfactual Post-Training for Self-Play Driving Policies
Jiarong Wei, Yin Wu, Runkai He +1
Self-play in high-throughput simulators yields driving policies with robust closed-loop performance, but improvement per unit of simulation diminishes as training scales. Policies…
TaPeR: Probabilistic Recovery of Sparse Task Precedence Graphs from a Handful of Demonstrations
Adrian Röfer, Karla Stepanova, Abhinav Valada
Long-horizon manipulation tasks are often only partially ordered. For example, when assembling an electronic device, the battery and circuit board may be installed in either order,…
Attention from Action, for Action: Emergent Visual Bottlenecks for Policy Learning
Zheyu Zhuang, Ruiyu Wang, Nick Heppert +4
Visual bottlenecks that focus policy inputs on regions of interest (ROIs) can improve data-efficient visuomotor learning by separating where to look from how to act. Many ROI inter…
Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives
Laura Londoño, Klaus Baumann, Abhinav Valada +1
Care robots are increasingly being introduced into healthcare settings, raising important questions about their acceptance and ethical implementation. To better understand these ch…
One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments
Jan Ole von Hartz, Abhinav Valada, Joschka Boedecker
Multi-stream robot manipulation policies achieve unparalleled sample efficiency and generalization by modeling actions relative to environmental reference frames. However, existing…
Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots
Roberto Corlito, Fabian Schmidt, Nils Seibert +3
Autonomous navigation of quadrupedal robots in diverse environments fundamentally relies on resilient Simultaneous Localization and Mapping (SLAM). While visual-inertial SLAM has m…
3D Scene Graphs: Open Challenges and Future Directions
Dennis Rotondi, Francesco Argenziano, Sebastian Koch +10
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Thei…
CoPark: Learning Reactive Parking via Self-Play
Jiarong Wei, Yanxing Chen, Sinuo Song +3
Learning a single policy that reaches a goal with high geometric precision while interacting safely with nearby agents poses conflicting objectives. Precision favors commitment to…
Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments
Julia Hindel, Simon Bultmann, Houman Masnavi +2
Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation behavior toward safe and effi…
Sparse3DTrack: Monocular 3D Object Tracking Using Sparse Supervision
Nikhil Gosala, B. Ravi Kiran, Senthil Yogamani +1
Monocular 3D object tracking aims to estimate temporally consistent 3D object poses across video frames, enabling autonomous agents to reason about scene dynamics. However, existin…
The Neural Compass: Probabilistic Relative Feature Fields for Robotic Search
Gabriele Somaschini, Adrian Röfer, Abhinav Valada
Object co-occurrences provide a key cue for finding objects successfully and efficiently in unfamiliar environments. Typically, one looks for cups in kitchens and views fridges as…
LAD-Drive: Bridging Language and Trajectory with Action-Aware Diffusion Transformers
Fabian Schmidt, Karol Fedurko, Markus Enzweiler +1
While multimodal large language models (MLLMs) provide advanced reasoning for autonomous driving, translating their discrete semantic knowledge into continuous trajectories remains…
Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search
Imen Mahdi, Matteo Cassinelli, Fabien Despinoy +2
Open-world interactive object search in household environments requires understanding semantic relationships between objects and their surrounding context to guide exploration effi…
ParkDiffusion++: Ego Intention Conditioned Joint Multi-Agent Trajectory Prediction for Automated Parking using Diffusion Models
Jiarong Wei, Anna Rehr, Christian Feist +1
Automated parking is a challenging operational domain for advanced driver assistance systems, requiring robust scene understanding and interaction reasoning. The key challenge is t…
SparTa: Sparse Graphical Task Models from a Handful of Demonstrations
Adrian Röfer, Nick Heppert, Abhinav Valada
Learning long-horizon manipulation tasks efficiently is a central challenge in robot learning from demonstration. Unlike recent endeavors that focus on directly learning the task i…
Articulated 3D Scene Graphs for Open-World Mobile Manipulation
Martin Büchner, Adrian Röfer, Tim Engelbracht +5
Semantic scene understanding has enabled robots to reason and perform affordance-driven object interaction. Despite this, real-world manipulation remains fundamentally limited by t…
Scaling Single Human Demonstrations for Imitation Learning using Generative Foundational Models
Nick Heppert, Minh Quang Nguyen, Abhinav Valada
Imitation learning is a popular paradigm to teach robots new tasks, but collecting robot demonstrations through teleoperation or kinesthetic teaching is tedious and time-consuming.…
Online Estimation and Manipulation of Articulated Objects
Russell Buchanan, Adrian Röfer, João Moura +2
From refrigerators to kitchen drawers, humans interact with articulated objects effortlessly every day while completing household chores. For automating these tasks, service robots…