3 citations · 4 across the 16 of their papers we have counts for
14 papers · 1 filter
Whole-Body Inverse Kinematics with Graph Diffusion
Helong Huang, Kai Tan, Feng Wen +2
Inverse kinematics (IK) is a fundamental problem in robotics, requiring the generation of joint configurations that satisfy target end-effector poses. Existing approaches often str…
ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching
Shuoheng Zhang, Yifu Yuan, Hongyao Tang +7
Existing imitation learning methods enable robots to interact autonomously with the physical environment. However, contact-rich manipulation tasks remain a significant challenge du…
ST-VLA: Enabling 4D-Aware Spatiotemporal Understanding for General Robot Manipulation
You Wu, Zixuan Chen, Cunxu Ou +9
Robotic manipulation in open-world environments requires reasoning across semantics, geometry, and long-horizon action dynamics. Existing hierarchical Vision-Language-Action (VLA)…
H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model
Jinbang Huang, Wenyuan Chen, Zhiyuan Li +9
World models are becoming central to robotic planning and control by predicting future state transitions. Existing approaches mainly rely on visual, latent, or language prediction,…
Do World Action Models Generalize Better than VLAs? A Robustness Study
Zhanguang Zhang, Zhiyuan Li, Behnam Rahmati +11
Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting how it will evolve in response…
More than A Point: Capturing Uncertainty with Adaptive Affordance Heatmaps for Spatial Grounding in Robotic Tasks
Xinyu Shao, Yanzhe Tang, Pengwei Xie +6
Many language-guided robotic systems rely on collapsing spatial reasoning into discrete points, making them brittle to perceptual noise and semantic ambiguity. To address this chal…
Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation
Jinbang Huang, Zhiyuan Li, Yuanzhao Hu +4
Large Language Models (LLMs) have recently shown strong promise for robotic task planning, particularly through automatic planning domain generation. However, prior approaches larg…
OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Yuecheng Liu, Dafeng Chi, Shiguang Wu +10
Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction,…
GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions
Helong Huang, Min Cen, Kai Tan +3
Vision-language-action models have emerged as a crucial paradigm in robotic manipulation. However, existing VLA models exhibit notable limitations in handling ambiguous language in…
Ark: An Open-source Python-based Framework for Robot Learning
Magnus Dierking, Christopher E. Mower, Sarthak Das +10
Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind…
AutoLayout: Closed-Loop Layout Synthesis via Slow-Fast Collaborative Reasoning
Weixing Chen, Dafeng Chi, Yang Liu +7
The automated generation of layouts is vital for embodied intelligence and autonomous systems, supporting applications from virtual environment construction to home robot deploymen…
Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation
Lingfeng Zhang, Yuecheng Liu, Zhanguang Zhang +19
Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, enabling agents to leverage commonsense…
SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and Chain-of-Thought for Embodied Task Planning
Yuecheng Liu, Dafeng Chi, Shiguang Wu +13
Spatial reasoning is an essential problem in embodied AI research. Efforts to enhance spatial reasoning abilities through supplementary spatial data and fine-tuning have proven lim…
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Christopher E. Mower, Yuhui Wan, Hongzhan Yu +20
We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our sys…