Tootfinder

Sources: Foxconn and Nvidia are in talks to deploy humanoid robots at a new Foxconn factory in Houston by Q1 2026 to produce Nvidia AI servers (Wen-Yee Lee/Reuters)
https://www.reuters.com/world/china/nvidia

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Tianlin Zhang, Linzhu Yue, Hongbo Zhang, Lingwei Zhang, Xuanqi Zeng, Zhitao Song, Yun-Hui Liu
https://arxiv.org/abs/2506.14278

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Humanoid robots often face significant balance issues due to the motion of their heavy limbs. These challenges are particularly pronounced when attempting dynamic motion or operating in environments with irregular terrain. To address this challenge, this manuscript proposes a whole-body control framework for humanoid robots with heavy limbs, using a model-based approach that combines a kino-dynamics planner and a hierarchical optimization problem. The kino-dynamics planner is designed as a mode…

Source: Amazon is developing software for humanoid robots to deliver packages and is near completion of an indoor "humanoid park" in San Francisco to test them (Rocket Drew/The Information)
https://www.theinformation.com/articles/am

Amazon Prepares to Test Humanoid Robots for Delivering Packages
Amazon is developing software for humanoid robots that could eventually take the jobs of delivery workers, according to a person who has been involved in the effort. In doing so, Amazon is paving the way to automate a major part of its operation, the delivery of parcels around the world.

GMT: General Motion Tracking for Humanoid Whole-Body Control
Zixuan Chen, Mazeyu Ji, Xuxin Cheng, Xuanbin Peng, Xue Bin Peng, Xiaolong Wang
https://arxiv.org/abs/2506.14770

GMT: General Motion Tracking for Humanoid Whole-Body Control
The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in t…

Hugging Face unveils two open-source humanoid robots, the $3,000 full-sized HopeJR and a $250-$300 Reachy Mini desktop unit, expected to ship by the end of 2025 (Rebecca Szkutak/TechCrunch)
https://techcrunch.com/2025/05/29/hugging-face-unveils-two-…

Hugging Face unveils two new humanoid robots | TechCrunch
Hugging Face hopes to start shipping two new open-source humanoid robots by the end of 2025.

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
Bolin Li, Linwei Sun, Xuecong Huang, Yuzhi Jiang, Lijun Zhu
https://arxiv.org/abs/2506.08416

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
This paper presents a periodic bipedal gait learning method using reward composition, integrated with a real-time gait planner for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's lea…

Why would ASI *build* robots? Why not grow fleshbots?
#postlaboreconomics

Humanoid World Models: Open World Foundation Models for Humanoid Robotics
Muhammad Qasim Ali, Aditya Sridhar, Shahbuland Matiana, Alex Wong, Mohammad Al-Sharman
https://arxiv.org/abs/2506.01182

Humanoid World Models: Open World Foundation Models for Humanoid Robotics
Humanoid robots have the potential to perform complex tasks in human centered environments but require robust predictive models to reason about the outcomes of their actions. We introduce Humanoid World Models (HWM) a family of lightweight open source video based models that forecast future egocentric observations conditioned on actions. We train two types of generative models Masked Transformers and FlowMatching on 100 hours of humanoid demonstrations. Additionally we explore architectural var…

MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains
Dewei Wang, Xinmiao Wang, Xinzhe Liu, Jiyuan Shi, Yingnan Zhao, Chenjia Bai, Xuelong Li
https://arxiv.org/abs/2506.08840

MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains
Humanoid robots have demonstrated robust locomotion capabilities using Reinforcement Learning (RL)-based approaches. Further, to obtain human-like behaviors, existing methods integrate human motion-tracking or motion prior in the RL framework. However, these methods are limited in flat terrains with proprioception only, restricting their abilities to traverse challenging terrains with human-like gaits. In this work, we propose a novel framework using a mixture of latent residual experts with mu…

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Kwanwoo Lee, Gyeongjae Park, Jaeheung Park
https://arxiv.org/abs/2506.03856

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Balance control for humanoid robots has been extensively studied to enable robots to navigate in real-world environments. However, balance controllers that explicitly optimize the durations of both the single support phase, also known as step timing, and the Double Support Phase (DSP) have not been widely explored due to the inherent nonlinearity of the associated optimization problem. Consequently, many recent approaches either ignore the DSP or adjust its duration based on heuristics or on li…

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Lingfan Bao, Yan Pan, Tianhu Peng, Chengxu Zhou
https://arxiv.org/abs/2506.01563

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on fixed motion libraries or computationally expensive generative models. We propose a hierarchical framework that combines intention-aware reasoning via in-context learning (ICL) with real-time motion generation using diffusion models. Our system introduces structured prompting with confidence scoring, fallback behavio…

This https://arxiv.org/abs/2411.14321 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots
The control of legged robots, particularly humanoid and quadruped robots, presents significant challenges due to their high-dimensional and nonlinear dynamics. While linear systems can be effectively controlled using methods like Model Predictive Control (MPC), the control of nonlinear systems remains complex. One promising solution is the Koopman Operator, which approximates nonlinear dynamics with a linear model, enabling the use of proven linear control techniques. However, achieving accurat…

This https://arxiv.org/abs/2505.24266 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SignBot: Learning Human-to-Humanoid Sign Language Interaction
Sign language is a natural and visual form of language that uses movements and expressions to convey meaning, serving as a crucial means of communication for individuals who are deaf or hard-of-hearing (DHH). However, the number of people proficient in sign language remains limited, highlighting the need for technological advancements to bridge communication gaps and foster interactions with minorities. Based on recent advancements in embodied humanoid robots, we propose SignBot, a novel framew…

This https://arxiv.org/abs/2504.14305 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning
Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches that mimic whole-body motions often neglect the distinct roles of upper and lower body. This oversight leads to computationally intensive policy learning and frequently causes robot instability and falls during real-world execution. To address these issues, we propose Adversarial Locomotion and Motion Imitation (ALMI…

This https://arxiv.org/abs/2505.22642 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control
Reinforcement learning (RL) has driven significant progress in robotics, but its complexity and long training times remain major bottlenecks. In this report, we introduce FastTD3, a simple, fast, and capable RL algorithm that significantly speeds up training for humanoid robots in popular suites such as HumanoidBench, IsaacLab, and MuJoCo Playground. Our recipe is remarkably simple: we train an off-policy TD3 agent with several modifications -- parallel simulation, large-batch updates, a distri…