14 papers
Hilbert-Scale Stability of Posterior Diffusion Priors for Ill-Posed Signal Recovery
Bahman Moraffah, Raha Moraffah
Indirect and noisy observations are difficult to invert when the forward operator smooths out the unknown signal. In this case, the unstable modes are precisely the modes that the…
Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking
Tao Huang, Ruofei Liu, Xuchen Tang +15
Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance…
RoboStriker: Latent-Space Strategic Games for Autonomous Humanoid Boxing
Kangning Yin, Kaige Liu, Zhe Cao +9
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a profound challenge, particularly in contact-rich and highly dynamic tasks such as b…
ReactiveBFM: Reactive Closed-Loop Motion Planning Towards Universal Humanoid Whole-Body Control
Xiao Chen, Weishuai Zeng, Xiaojie Niu +12
While current Behavior Foundation Models (BFMs) provide robust control priors for humanoids, they only execute pre-defined reference motions. As a result, they are vulnerable to en…
Scaling Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Kangning Yin, Xiaojie Niu +15
Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making i…
Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors
Jiahe Chen, ZiRui Wang, Feiyu Jia +7
Whole-body Humanoid-Object Interaction (HOI) is bottlenecked by the scarcity of high-fidelity 3D data. While video generative priors offer a promising alternative, existing methods…
RoboStriker: Hierarchical Decision-Making for Autonomous Humanoid Boxing
Kangning Yin, Zhe Cao, Wentao Dong +7
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a major challenge, particularly in contact-rich and highly dynamic tasks such as boxi…
Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Shunlin Lu, Kangning Yin +4
Whole-body control (WBC) of humanoid robots has witnessed remarkable progress in skill versatility, enabling a wide range of applications such as locomotion, teleoperation, and mot…
UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robots
Kangning Yin, Weishuai Zeng, Ke Fan +7
Achieving expressive and generalizable whole-body motion control is essential for deploying humanoid robots in real-world environments. In this work, we propose UniTracker, a three…
JAEGER: Dual-Level Humanoid Whole-Body Controller
Ziluo Ding, Haobin Jiang, Yuxuan Wang +7
This paper presents JAEGER, a dual-level whole-body controller for humanoid robots that addresses the challenges of training a more robust and versatile policy. Unlike traditional…
RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
Junpeng Yue, Zepeng Wang, Yuxuan Wang +7
This paper focuses on a critical challenge in robotics: translating text-driven human motions into executable actions for humanoid robots, enabling efficient and cost-effective lea…
From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
Yuxuan Wang, Ming Yang, Ziluo Ding +5
Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in trainin…
Scaling Large Motion Models with Million-Level Human Motions
Ye Wang, Sipeng Zheng, Bin Cao +4
Inspired by the recent success of LLMs, the field of human motion understanding has increasingly shifted toward developing large motion models. Despite some progress, current effor…
Egocentric Vision Language Planning
Zhirui Fang, Ming Yang, Weishuai Zeng +5
We explore leveraging large multi-modal models (LMMs) and text2image models to build a more general embodied agent. LMMs excel in planning long-horizon tasks over symbolic abstract…