24 citations · 100 across the 25 of their papers we have counts for
27 papers
EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics
Haoxiang You, Zeyu Shen, Yilang Liu +16
We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot policies. To test this, we d…
HIGenNTO: Scalable Humanoid Interaction Generation via Noise-Space Trajectory Optimization
Lalit Jayanti, Kashu Yamazaki, Yuto Shibata +2
Humanoid robots can acquire complex skills by imitating kinematic humanoid motion references, yet reliable references for contact-rich interactions remain difficult to obtain: moti…
Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks
Trinity Chung, Kashu Yamazaki, Dhruv Patel +4
Tactile sensing is critical for contact-rich dexterous manipulation, yet it remains unclear which tactile abstractions a policy needs and when richer tactile fields justify their h…
Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
Yuto Shibata, Kashu Yamazaki, Lalit Jayanti +3
Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking within physics engines (GMT) have…
ExtremControl: Low-Latency Humanoid Teleoperation with Scaled Velocity Feedforward
Ziyan Xiong, Lixing Fang, Junyun Huang +3
Building a low-latency humanoid teleoperation system is essential for collecting diverse reactive and dynamic demonstrations. However, end-to-end latencies in existing approaches c…
Rethinking Progression of Memory State in Robotic Manipulation: An Object-Centric Perspective
Nhat Chung, Taisei Hanyu, Toan Nguyen +9
As embodied agents operate in increasingly complex environments, the ability to perceive, track, and reason about individual object instances over time becomes essential, especiall…
SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation
Taisei Hanyu, Nhat Chung, Huy Le +10
Inspired by how humans reason over discrete objects and their relationships, we explore whether compact object-centric and object-relation representations can form a foundation for…
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…
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model
Khoa Vo, Thinh Phan, Kashu Yamazaki +2
Current video-language models (VLMs) rely extensively on instance-level alignment between video and language modalities, which presents two major limitations: (1) visual reasoning…
-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Xiang Li, Kai Qiu, Jinglu Wang +6
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the…
Open-Fusion: Real-time Open-Vocabulary 3D Mapping and Queryable Scene Representation
Kashu Yamazaki, Taisei Hanyu, Khoa Vo +5
Precise 3D environmental mapping is pivotal in robotics. Existing methods often rely on predefined concepts during training or are time-intensive when generating semantic maps. Thi…
AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
Kashu Yamazaki, Taisei Hanyu, Minh Tran +7
Aerial Image Segmentation is a top-down perspective semantic segmentation and has several challenging characteristics such as strong imbalance in the foreground-background distribu…
Contextual Explainable Video Representation: Human Perception-based Understanding
Khoa Vo, Kashu Yamazaki, Phong X. Nguyen +3
Video understanding is a growing field and a subject of intense research, which includes many interesting tasks to understanding both spatial and temporal information, e.g., action…
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection
Hyekang Kevin Joo, Khoa Vo, Kashu Yamazaki +1
Video anomaly detection (VAD) -- commonly formulated as a multiple-instance learning problem in a weakly-supervised manner due to its labor-intensive nature -- is a challenging pro…
VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning
Kashu Yamazaki, Khoa Vo, Sang Truong +2
Video paragraph captioning aims to generate a multi-sentence description of an untrimmed video with several temporal event locations in coherent storytelling. Following the human p…
AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation
Khoa Vo, Sang Truong, Kashu Yamazaki +3
Temporal action proposal generation (TAPG) is a challenging task, which requires localizing action intervals in an untrimmed video. Intuitively, we as humans, perceive an action th…
AISFormer: Amodal Instance Segmentation with Transformer
Minh Tran, Khoa Vo, Kashu Yamazaki +3
Amodal Instance Segmentation (AIS) aims to segment the region of both visible and possible occluded parts of an object instance. While Mask R-CNN-based AIS approaches have shown pr…
VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning
Kashu Yamazaki, Sang Truong, Khoa Vo +4
In this paper, we leverage the human perceiving process, that involves vision and language interaction, to generate a coherent paragraph description of untrimmed videos. We propose…
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications
Viet-Khoa Vo-Ho, Kashu Yamazaki, Hieu Hoang +2
Deep learning methods have been successful in solving tasks in machine learning and have made breakthroughs in many sectors owing to their ability to automatically extract features…
ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation
Khoa Vo, Kashu Yamazaki, Sang Truong +3
Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of v…
AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation
Khoa Vo, Hyekang Joo, Kashu Yamazaki +4
Humans typically perceive the establishment of an action in a video through the interaction between an actor and the surrounding environment. An action only starts when the main ac…
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey
Ngan Le, Vidhiwar Singh Rathour, Kashu Yamazaki +2
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remar…
Agent-Environment Network for Temporal Action Proposal Generation
Viet-Khoa Vo-Ho, Ngan Le, Kashu Yamazaki +2
Temporal action proposal generation is an essential and challenging task that aims at localizing temporal intervals containing human actions in untrimmed videos. Most of existing a…
Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
Vidhiwar Singh Rathour, Kashu Yamakazi, T. Hoang Ngan Le
Medical image segmentation is one of the most challenging tasks in medical image analysis and has been widely developed for many clinical applications. Most of the existing metrics…