1 citations · 1 across the 11 of their papers we have counts for
13 papers
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
Zechu Li, Yufeng Jin, Xiaoyang Liu +4
Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pip…
Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
Rickmer Krohn, Erik Helmut, Niklas Funk +3
Touch sensing is beneficial for solving a wide variety of manipulation tasks. While there exists a wide range of tactile sensors with different properties, exploiting the fusion of…
Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
Nishad Singhi, Christian Bialas, Snehal Jauhri +4
Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models (MLLMs) have significantly a…
Nautilus: From One Prompt to Plug-and-Play Robot Learning
Yufeng Jin, Jianfei Guo, Xiaogang Jia +8
Robot learning research is fragmented across policy families, benchmark suites, and real robots; each implementation is entangled with the others in a complex combination matrix, m…
Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers
Snehal Jauhri, Vignesh Prasad, Georgia Chalvatzaki
Mobile Manipulation (MoMa) of articulated objects, such as opening doors, drawers, and cupboards, demands simultaneous, whole-body coordination between a robot's base and arms. Cla…
Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning
Fabian Hahne, Vignesh Prasad, Georgia Chalvatzaki +2
Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localization and arm allocation problem…
Bimanual Robot Manipulation via Multi-Agent In-Context Learning
Alessio Palma, Indro Spinelli, Vignesh Prasad +4
Large Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf, text-only LLMs to pred…
Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning
Franziska Herbert, Vignesh Prasad, Han Liu +2
Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary…
SE(3)-PoseFlow: Estimating 6D Pose Distributions for Uncertainty-Aware Robotic Manipulation
Yufeng Jin, Niklas Funk, Vignesh Prasad +4
Object pose estimation is a fundamental problem in robotics and computer vision, yet it remains challenging due to partial observability, occlusions, and object symmetries, which i…
Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
Rickmer Krohn, Vignesh Prasad, Gabriele Tiboni +1
Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such…
The Role of Embodiment in Intuitive Whole-Body Teleoperation for Mobile Manipulation
Sophia Bianchi Moyen, Rickmer Krohn, Sophie Lueth +4
Intuitive Teleoperation interfaces are essential for mobile manipulation robots to ensure high quality data collection while reducing operator workload. A strong sense of embodimen…
2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos
Marvin Heidinger, Snehal Jauhri, Vignesh Prasad +1
When interacting with objects, humans effectively reason about which regions of objects are viable for an intended action, i.e., the affordance regions of the object. They can also…
6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting
Yufeng Jin, Vignesh Prasad, Snehal Jauhri +2
Efficient and accurate object pose estimation is an essential component for modern vision systems in many applications such as Augmented Reality, autonomous driving, and robotics.…