1 citations · 3 across the 21 of their papers we have counts for
26 papers
Find Something You Can't Do: Agentic Real-World Reinforcement Learning for Self-Improving VLA Models
Yuan Fang, Zechu Li, Haolei Tong +2
Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve from their own failures. Real-…
PolyUMI: Accessible Visual-Tactile-Audio Data Collection for Object Inference and Manipulation
Conor W. Hayes, Rickmer Krohn, Aravind Ramaswami +6
Humans typically rely on vision, touch, hearing, and proprioception to perceive contact and adapt their actions during manipulation. Providing robots with comparable responsiveness…
Contact-Rich Motion Planning via GPU-Parallel Mode Evaluation
Jiayun Li, Georgia Chalvatzaki
Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions. Existing m…
Globalized Constrained Stein Variational Inference for Diverse Feasible Robot Motion Planning
Jiayun Li, Georgia Chalvatzaki
Robot motion planning is inherently multimodal, yet classical planners typically return only a single solution. Probabilistic formulations address this limitation by maintaining a…
IMPACT: An Implicit Active-Set Augmented Lagrangian for Fast Contact-Implicit Trajectory Optimization
Jiayun Li, Dejian Gong, Georgia Chalvatzaki
Contact-implicit trajectory optimization (CITO) has attracted growing attention as a unified framework for planning and control in contact-rich robotic tasks. Recent approaches hav…
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…
Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation
Max Siebenborn, Daniel Ordoñez Apraez, Sophie Lueth +4
Mobile manipulation requires coordinated control of high-dimensional, bimanual robots. Imitation learning methods have been broadly used to solve these robotic tasks, yet typically…
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…
MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
Abhay Deshpande, Maya Guru, Rose Hendrix +23
A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or…
Stein Variational Ergodic Surface Coverage with SE(3) Constraints
Jiayun Li, Yufeng Jin, Sangli Teng +2
Surface manipulation tasks require robots to generate trajectories that comprehensively cover complex 3D surfaces while maintaining precise end-effector poses. Existing ergodic tra…
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Yejin Kim, Wilbert Pumacay, Omar Rayyan +23
Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that charac…
Robot-DIFT: Correspondence-Sensitive Diffusion Features for Contact-Rich Robot Manipulation
Yu Deng, Yufeng Jin, Xiaogang Jia +3
Robot manipulation often fails in the final millimeters: a policy may recognize the right object yet miss the pose offsets, boundaries, or pre-contact alignments needed for action.…
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…
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…
UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene
Christian Maurer, Snehal Jauhri, Sophie Lueth +1
Comprehensive visual, geometric, and semantic understanding of a 3D scene is crucial for successful execution of robotic tasks, especially in unstructured and complex environments.…
Adaptive Diffusion Constrained Sampling for Bimanual Robot Manipulation
Haolei Tong, Yuezhe Zhang, Sophie Lueth +1
Coordinated multi-arm manipulation requires satisfying multiple simultaneous geometric constraints across high-dimensional configuration spaces, which poses a significant challenge…
Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation
Zechu Li, Yufeng Jin, Daniel Ordonez Apraez +3
Humans naturally exhibit bilateral symmetry in their gross manipulation skills, effortlessly mirroring simple actions between left and right hands. Bimanual robots-which also featu…
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.…