arXiv:2509. 26633v3 Announce Type: replace-cross Abstract: A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies.
arXiv:2606. 08253v1 Announce Type: cross Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately.
arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.
The paper proposes a lightweight foothold prediction model (FPM) that anticipates future foot placements using only proprioception and velocity commands, allowing the robot to sample elevation maps only around predicted footholds. This preprocessing reduces redundant sensory input, enabling the locomotion policy to learn faster—reaching a given terrain difficulty in about one‑third the training iterations compared to using the full elevation map. The resulting policy outperforms both full‑map and attention‑based encoders on sparse foothold scenarios, and it zero‑shot transfers to a Unitree G1 humanoid across diverse indoor and outdoor obstacles.
arXiv:2601.07718v2 Announce Type: replace-cross Abstract: Achieving robust humanoid locomotion in complex, unstructured environments requires transitioning from reactive proprioception to proactive p...
arXiv:2510. 18348v2 Announce Type: replace-cross Abstract: State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait priors that constrain the action space, bias policy optimization, and limit adaptability across robot morphologies, or (ii) operate "blind," making them unable to anticipate hind-leg terrain and brittle to observation noise.
arXiv:2607. 02431v1 Announce Type: cross Abstract: Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations.
HumanX is a unified framework that transforms human videos into generalizable, real‑world interaction skills for humanoid robots without task‑specific design. It combines XGen, a data‑generation pipeline that creates physically plausible robot interaction data from monocular video, with XMimic, a learning framework that extracts interaction priors from only seconds of demonstration. Across five domains—basketball, football, badminton, cargo pickup, and reactive fighting—HumanX learns nine distinct skills and transfers them zero‑shot to a physical Unitree G1 humanoid, achieving complex maneuvers and sustained passing sequences from a single video.
arXiv:2610.34126v1 Announce Type: cross Abstract: Humanoid robots have recently achieved remarkable success in motion tracking. However, during real-world deployment, robots inevitably encounter dyna...
arXiv:2606. 30645v1 Announce Type: cross Abstract: Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion.
The paper introduces ASTRIL-MPC, a language‑guided neural model predictive control framework that enables articulated tracked robots to navigate complex, contact‑rich urban environments such as stairwells and cluttered interiors. By combining a learned kinematics model that predicts short‑horizon state changes, an optimization‑based planner with multi‑objective costs, and a large language model that safely updates control weights, the system achieves up to 71% better traversal quality than non‑adaptive NMPC and 67% better than a PPO baseline, while eliminating collision impacts during descent. Real‑robot trials over four indoor obstacles confirm the method’s transferability to physical contact‑rich traversal.
arXiv:2601. 15995v2 Announce Type: replace-cross Abstract: Parkour tasks for quadrupeds have emerged as a promising benchmark for agile locomotion.