TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System

arXiv — cs.CVWednesday, November 5, 2025 at 5:00:00 AM
TWIST2 is a humanoid teleoperation and data collection system designed to be scalable, portable, and cost-effective, addressing limitations of traditional methods that depend on expensive motion capture setups. This innovative system offers a holistic approach to gathering large-scale data in robotics, which is essential for advancing humanoid robotics research. Unlike conventional techniques, TWIST2 enables more accessible and comprehensive data collection, potentially accelerating developments in the field. Its portability and affordability make it a practical alternative for researchers seeking to collect extensive humanoid motion data without the high costs associated with traditional equipment. The system's design aims to facilitate broader data acquisition efforts, supporting the growing demand for robust datasets in humanoid robotics. While claims suggest that TWIST2 advances humanoid robotics, these remain unverified based on the current evidence. Overall, TWIST2 represents a promising step toward more efficient and inclusive data collection methodologies in robotics.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps
Continue Readings
Learning-based Multi-View Stereo: A Survey
NeutralArtificial Intelligence
A recent survey on learning-based Multi-View Stereo (MVS) techniques highlights the advancements in 3D reconstruction, which is crucial for applications such as Augmented and Virtual Reality, autonomous driving, and robotics. The study categorizes these methods into depth map-based, voxel-based, NeRF-based, and others, emphasizing the effectiveness of depth map-based approaches.
SPARK: Scalable Real-Time Point Cloud Aggregation with Multi-View Self-Calibration
PositiveArtificial Intelligence
A new framework named SPARK has been introduced for scalable real-time multi-camera point cloud aggregation, addressing challenges in 3D reconstruction, particularly in handling extrinsic uncertainty and multi-view fusion. This innovative approach combines geometry-aware online extrinsic estimation with a confidence-driven point cloud fusion strategy, enabling stable point cloud generation in dynamic environments.
On the Sample Complexity of Differentially Private Policy Optimization
NeutralArtificial Intelligence
A recent study on differentially private policy optimization (DPPO) has been published, focusing on the sample complexity of policy optimization (PO) in reinforcement learning (RL). This research addresses privacy concerns in sensitive applications such as robotics and healthcare by formalizing a definition of differential privacy tailored to PO and analyzing the sample complexity of various PO algorithms under DP constraints.
Simulating the Visual World with Artificial Intelligence: A Roadmap
NeutralArtificial Intelligence
The landscape of video generation is evolving, transitioning from merely creating visually appealing clips to constructing interactive virtual environments that adhere to physical plausibility. This shift is highlighted in a recent survey that conceptualizes modern video foundation models as a combination of implicit world models and video renderers, enabling coherent visual reasoning and task planning.

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about