204 citations · 331 across the 14 of their papers we have counts for
12 papers · 1 filter
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models
Chenyuan Zhou, Yining Hong, Huang Huang +9
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitl…
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
Wensi Ai, Christopher Luey, Karthik Dharmarajan +7
Policies trained offline from teleoperated demonstrations can degrade outside their training distribution. This gap is particularly acute in long-horizon whole-body mobile manipula…
RoboTTT: Context Scaling for Robot Policies
Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng +8
Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training re…
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
Evans Han, Yunfan Jiang, Yingke Wang +6
Recent advances in robot imitation learning have produced powerful visuomotor policies that manipulate diverse objects from visual inputs. However, monocular observations lack dept…
SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
Nadun Ranawaka, Josiah Wong, Wei-Lin Pai +15
Training and evaluating robot policies in the real world is costly and difficult to scale. We introduce SimFoundry, a modular and automated system for zero-shot real-to-sim scene c…
EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data
Ruijie Zheng, Dantong Niu, Yuqi Xie +12
Human behavior is among the most scalable sources of data for learning physical intelligence, yet how to effectively leverage it for dexterous manipulation remains unclear. While p…
MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation
Chengshu Li, Mengdi Xu, Arpit Bahety +11
Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming. This ch…
BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities
Yunfan Jiang, Ruohan Zhang, Josiah Wong +7
Real-world household tasks present significant challenges for mobile manipulation robots. An analysis of existing robotics benchmarks reveals that successful task performance hinge…
Automated Creation of Digital Cousins for Robust Policy Learning
Tianyuan Dai, Josiah Wong, Yunfan Jiang +5
Training robot policies in the real world can be unsafe, costly, and difficult to scale. Simulation serves as an inexpensive and potentially limitless source of training data, but…
TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
Yunfan Jiang, Chen Wang, Ruohan Zhang +2
Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulat…
VIMA: General Robot Manipulation with Multimodal Prompts
Yunfan Jiang, Agrim Gupta, Zichen Zhang +7
Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task speci…