3 citations · 3 across the 13 of their papers we have counts for
17 papers
PrefPI: Preference-Guided Steering into Out-of-Distribution Behaviors
Seungeun Rho, Wontaek Kim, Danfei Xu +1
We present PrefPI (Preference-Guided Policy Iteration), an iterative framework for steering pretrained generative robot policies using only relative preferences over self-generated…
Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation
Fukang Liu, Yipu Chen, Jaehwi Jang +3
Humanoid robots are expected to perform diverse human-level tasks in daily environments, many of which require precise regulation of interaction forces. While recent vision-languag…
World-Action Models for Robot Learning and Control: A Survey
Zuxing Lu, Hongjia Zhai, Guanzhi Wang +13
Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations and language instructions to act…
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
Xinghao Zhu, Zixi Liu, Shalin Jain +18
Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging. We present Contact…
EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data
Baoyu Li, Xinchen Yin, Mengying Lin +2
Egocentric human data offers scalable supervision for robot manipulation. However, behavior cloning entangles transferable content like objects, scenes, and task semantics, with no…
GRAFT: Graph-Based Affordance Transfer via Part Correspondence
Mengying Lin, Utkarsh Mishra, Ajay Mandlekar +1
Generalizing robotic manipulation to unseen objects remains challenging, as learning-based approaches require many demonstrations and fail in few-shot settings. Prior work transfer…
Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation
Kuancheng Wang, Vaibhav Saxena, Shuo Cheng +2
Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both search and act, and learning this…
Energy-based Compositional Diffusion Planning
Tao Sun, Utkarsh Aashu Mishra, Jiaxin Lu +2
Compositional diffusion planners aim to solve long-horizon robotic tasks using short training trajectories. Yet, current approaches often rely on the heuristic stitching of local p…
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…
Contrastive Action-Image Pre-training for Visuomotor Control
Yuvan Sharma, Dantong Niu, Anirudh Pai +16
Existing vision encoders for robotics face a fundamental bottleneck: robotic datasets lack the scale necessary for large-scale pre-training. Prior work circumvents this data scarci…
KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
Yixuan Huang, Bowen Li, Vaibhav Saxena +9
Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand.…
EgoAVFlow: Robot Policy Learning with Active Vision from Human Egocentric Videos via 3D Flow
Daesol Cho, Youngseok Jang, Danfei Xu +1
Egocentric human videos provide a scalable source of manipulation demonstrations; however, deploying them on robots requires active viewpoint control to maintain task-critical visi…
Reference Grounded Skill Discovery
Seungeun Rho, Aaron Trinh, Danfei Xu +1
Scaling unsupervised skill discovery algorithms to high-DoF agents remains challenging. As dimensionality increases, the exploration space grows exponentially, while the manifold o…
EMMA: Scaling Mobile Manipulation via Egocentric Human Data
Lawrence Y. Zhu, Pranav Kuppili, Ryan Punamiya +5
Scaling mobile manipulation imitation learning is bottlenecked by expensive mobile robot teleoperation. We present Egocentric Mobile MAnipulation (EMMA), an end-to-end framework tr…
Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation
Fukang Liu, Zhaoyuan Gu, Yilin Cai +8
Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex c…
C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning
Vaibhav Saxena, Yotto Koga, Danfei Xu
Behavior Cloning (BC) methods are effective at learning complex manipulation tasks. However, they are prone to spurious correlation - expressive models may focus on distractors tha…
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation
Kelin Yu, Yunhai Han, Qixian Wang +3
Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. Prior research has shown the possibility of learning tactile-guided po…