1 citations · 1 across the 10 of their papers we have counts for
13 papers
RoboVista: Evaluating Vision Language Models for Diverse Robot Applications
Shuangyu Xie, Kaiyuan Chen, Ziyang Chen +8
Diverse applications for robotics, such as industry and agriculture, require robots to operate across various embodiments, changing visual conditions, and complex planning. Vision-…
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.…
Learning Adaptive Reasoning Paths for Efficient Visual Reasoning
Yixu Huang, Tinghui Zhu, Muhao Chen
Visual reasoning models (VRMs) have recently shown strong cross-modal reasoning capabilities by integrating visual perception with language reasoning. However, they often suffer fr…
LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer
Lihan Zha, Asher J. Hancock, Mingtong Zhang +5
A long-standing goal in robotics is a generalist policy that can be deployed zero-shot on new robot embodiments without per-embodiment adaptation. Despite large-scale multi-embodim…
Composable Visual Tokenizers with Generator-Free Diagnostics of Learnability
Bingchen Zhao, Qiushan Guo, Ye Wang +3
We introduce CompTok, a training framework for learning visual tokenizers whose tokens are enhanced for compositionality. CompTok uses a token-conditioned diffusion decoder. By emp…
Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems
Zengyu Zou, Jingyuan Wang, Yixuan Huang +1
This paper addresses the cooperative Multi-Vehicle Dynamic Pickup and Delivery Problem with Stochastic Requests (MVDPDPSR) and proposes an end-to-end centralized decision-making fr…
Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models
Mingen Li, Houjian Yu, Yixuan Huang +3
Long-horizon routing tasks of deformable linear objects (DLOs), such as cables and ropes, are common in industrial assembly lines and everyday life. These tasks are particularly ch…
Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference
Yixuan Huang, Novella Alvina, Mohanraj Devendran Shanthi +1
Skill effect models for long-horizon manipulation tasks are prone to failures in conditions not covered by training data distributions. Therefore, enabling robots to reason about a…
Points2Plans: From Point Clouds to Long-Horizon Plans with Composable Relational Dynamics
Yixuan Huang, Christopher Agia, Jimmy Wu +2
We present Points2Plans, a framework for composable planning with a relational dynamics model that enables robots to solve long-horizon manipulation tasks from partial-view point c…
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Yixuan Huang, Jialin Yuan, Chanho Kim +4
Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-orient…
Latent Space Planning for Multi-Object Manipulation with Environment-Aware Relational Classifiers
Yixuan Huang, Nichols Crawford Taylor, Adam Conkey +2
Objects rarely sit in isolation in everyday human environments. If we want robots to operate and perform tasks in our human environments, they must understand how the objects they…
Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers
Yixuan Huang, Adam Conkey, Tucker Hermans
Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change a…
Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots
Yixuan Huang, Michael Bentley, Tucker Hermans +1
Tendon-driven robots, a type of continuum robot, have the potential to reduce the invasiveness of surgery by enabling access to difficult-to-reach anatomical targets. In the future…