4 citations · 5 across the 9 of their papers we have counts for
13 papers
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion
Chih-Hai Su, Tsung-Wei Ke, Yu-Lun Liu
Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements. Residual priors trained on one geometry family frequently fail to beat parameter-free…
Task Inference Beyond Least Squares in Behavioral Foundation Models
Kuan-Hsun Tu, Chien-Sheng Chiang, Hsin-Wei Chen +2
Behavioral Foundation Models (BFMs) aim to solve a wide range of downstream tasks without test-time policy learning by inferring a task vector from the reward function. While effic…
Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching
Kuan-Hsun Tu, Hsuan-Chi Liu, Jia-Wei Liao +3
We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas standard conditional flow match…
TaRL: Learning General and Physical Rewards from Tactile Demonstrations
Po-Yi Wu, Dao-Jan Chang, Shang-Ya Hsiao +3
Contact-rich manipulation requires robots to sequence precise contacts, maintain stable grasps, and apply directed forces. Reinforcement learning (RL) can acquire such behaviors au…
COT-FM: Cluster-wise Optimal Transport Flow Matching
Chiensheng Chiang, Kuan-Hsun Tu, Jia-Wei Liao +2
We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved tra…
DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos
Jhen Hsieh, Kuan-Hsun Tu, Kuo-Han Hung +1
We present DexMan, an automated framework that converts human visual demonstrations into bimanual dexterous manipulation skills for humanoid robots in simulation. Operating directl…
Information Seeking for Robust Decision Making under Partial Observability
Djengo Cyun-Jyun Fang, Tsung-Wei Ke
Explicit information seeking is essential to human problem-solving in practical environments characterized by incomplete information and noisy dynamics. When the true environmental…
RobotArena : Scalable Robot Benchmarking via Real-to-Sim Translation
Yash Jangir, Yidi Zhang, Pang-Chi Lo +7
The pursuit of robot generalists, agents capable of performing diverse tasks across diverse environments, demands rigorous and scalable evaluation. Yet real-world testing of robot…
See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation
Chih Yao Hu, Yang-Sen Lin, Yuna Lee +7
We present See, Point, Fly (SPF), a training-free aerial vision-and-language navigation (AVLN) framework built atop vision-language models (VLMs). SPF is capable of navigating to a…
3D FlowMatch Actor: Unified 3D Policy for Single- and Dual-Arm Manipulation
Nikolaos Gkanatsios, Jiahe Xu, Matthew Bronars +3
We present 3D FlowMatch Actor (3DFA), a 3D policy architecture for robot manipulation that combines flow matching for trajectory prediction with 3D pretrained visual scene represen…
3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
Tsung-Wei Ke, Nikolaos Gkanatsios, Katerina Fragkiadaki
Diffusion policies are conditional diffusion models that learn robot action distributions conditioned on the robot and environment state. They have recently shown to outperform bot…
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
Brian Yang, Huangyuan Su, Nikolaos Gkanatsios +4
Diffusion models excel at modeling complex and multimodal trajectory distributions for decision-making and control. Reward-gradient guided denoising has been recently proposed to g…
Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback
Mihir Prabhudesai, Tsung-Wei Ke, Alexander C. Li +2
The advancements in generative modeling, particularly the advent of diffusion models, have sparked a fundamental question: how can these models be effectively used for discriminati…