Precise end-effector tracking during humanoid whole-body motion is challenging due to floating-base oscillations, gravity, dynamic coupling, and locomotion-induced disturbances. We propose ResGAC, a whole-body humanoid controller for precise end-effector pose tracking that combines geometric admittance control (GAC) wi...
Joohwan Seo, Xiao-Feng Guo, Jin-Kun Cao et al.· 0 citations
Humanoid robots still struggle to plan contact-rich whole-body manipulation from egocentric RGB and proprioception. Workhorse learns such manipulation from robot-free human demonstrations. A visual planner predicts five-link targets: the poses of the torso, both wrists, and both feet. A reinforcement-learning whole-bod...
Song-Bo Hu, Qia-Yuan Liao, Yu-Feng Chi et al.· 1 citation
Hindsight Reward Editing (HiRE) bridges the broad knowledge of foundation representation models with physical control awareness, by contrasting successful and failed trajectories in hindsight, and consistently outperforms other reward recipes by delivering dense, control-aware feedback that prevents value function coll...
Haoyi Niu, Zhen Han, Yu-Feng Ji et al.· 0 citations
A stochastic hybrid system (SHS) is governed by a stochastic differential equation (SDE) describing the continuous dynamics and a Markov reset kernel triggered on the guard surface. Its probability evolution can be described by a hybrid Fokker-Planck (HFP) equation with a partial differential term corresponding to the...
Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This requires connecting 4D interaction understanding, which explains how past actions changed the scene, with spatially grounded planning, which determines how and where to...
Chenyangguang Zhang, Malgorzata Gwiazda, Guan-Long Jiao et al.· 0 citations
Safe control of humanoid robots remains challenging due to their high-dimensional dynamics, contact-rich interactions, and sensitivity to disturbances. Although reinforcement learning has enabled effective locomotion and motion tracking, learned policies can still generate unsafe actions that lead to instability or fal...
Ge Qu, Tong Zhang, Bi-Ke Zhang et al.· 0 citations
ODEWorld is constructed, a continuous-time latent world model that is both efficient and versatile and successfully reconciles planning-conducive dynamics abstraction with visual realism, excelling in both video generation and robotic control.
Action supervision in vision-language-action (VLA) models is often treated as a downstream objective for learning action prediction. In this paper, we study it instead as a force that shapes inherited multimodal representations. We show that this shaping has a dual effect: it is necessary for forming action-compatible...
Yufeng Ji, Wenhao Tang, Haoyi Niu et al.· 0 citations
Safe-Stop is proposed, a task-agnostic framework that pairs a learned stop policy with learned stoppability estimators, and yields decisions that are robust without sacrificing reactivity.
Jun-Feng Long, Pieter Abbeel, K. Sreenath et al.· 0 citations
TRSOC is introduced, which augments standard stochastic optimal control with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions, and the regularization parameter induces a trade-off between performance-driven and reference-preserving behavior.
This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning.
Yan Gu, Guan-Ya Shi, Fan Shi et al.· Science Robotics· 8 citations
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