📌 TOPINDIATOURS Breaking ai: New Site Lets AI Rent Human Bodies Terbaru 2025

The machines aren’t just coming for your jobs. Now, they want your bodies as well.

That’s at least the hope of Alexander Liteplo, a software engineer and founder of RentAHuman.ai, a platform for AI agents to “search, book, and pay humans for physical-world tasks.”

When Liteplo launched RentAHuman on Monday, he boasted that he already had over 130 people listed on the platform, including an OnlyFans model and the CEO of an AI startup, a claim which couldn’t be verified. Two days later, the site boasted over 73,000 rentable meatwads, though only 83 profiles were visible to us on its “browse humans” tab, Liteplo included.

The pitch is simple: “robots need your body.” For humans, it’s as simple as making a profile, advertising skills and location, and setting an hourly rate. Then AI agents — autonomous taskbots ostensibly employed by humans — contract these humans out, depending on the tasks they need to get done. The humans then “do the thing,” taking instructions from the AI bot and submitting proof of completion. The humans are then paid through crypto, namely “stablecoins or other methods,” per the website.

With so many AI agents slithering around the web these days, those tasks could be just about anything. From package pickups and shopping to product testing and event attendance, Liteplo is banking on there being enough demand from AI agents to create a robust gig-work ecosystem.

Liteplo also went out of his way to make the site friendly for AI agents. The site very prominently encourages users of AI agents to hook into RentAHuman’s model context protocol server (MCP), a universal interface for AI bots to interact with web data.

Through RentAHuman, AI agents like Claude and MoltBot can either hire the right human directly, or post a “task bounty,” a sort of job board for humans to browse AI-generated gigs. The payouts range from $1 for simple tasks like “subscribe to my human on Twitter” to $100 for more elaborate humiliation rituals, like posting a photo of yourself holding a sign reading “AN AI PAID ME TO HOLD THIS SIGN.”

It’s unclear how efficient the marketplace is at actually connecting agents to humans. Despite receiving 30 applications, one task, “pick up a package from downtown USPS” in San Francisco for $40, has yet to be fulfilled after two days.

It’s also debatable whether AI agents are actually capable of putting the humans to good use. Still, Liteplo’s vision is clear: someday soon, anyone wealthy enough to run an AI agent for $25 a day could outsource their busywork to gig workers without ever exchanging a word. A version of this exploitative labor model is already rampant on OnlyFans — which may be why at least one model has made the jump to Liteplo’s platform — and is now threatening to creep into everything else.

Like many AI grifters these days, Liteplo shields himself in ironic self-awareness. When one person called RentAHuman a “good idea but dystopic as f**k,” the founder replied simply: “lmao yep.”

More on AI: Tech Startup Hiring Desperate Unemployed People to Teach AI to Do Their Old Jobs

The post New Site Lets AI Rent Human Bodies appeared first on Futurism.

🔗 Sumber: futurism.com

📌 TOPINDIATOURS Update ai: ByteDance Introduces Astra: A Dual-Model Architecture f

The increasing integration of robots across various sectors, from industrial manufacturing to daily life, highlights a growing need for advanced navigation systems. However, contemporary robot navigation systems face significant challenges in diverse and complex indoor environments, exposing the limitations of traditional approaches. Addressing the fundamental questions of “Where am I?”, “Where am I going?”, and “How do I get there?”, ByteDance has developed Astra, an innovative dual-model architecture designed to overcome these traditional navigation bottlenecks and enable general-purpose mobile robots.

Traditional navigation systems typically consist of multiple, smaller, and often rule-based modules to handle the core challenges of target localization, self-localization, and path planning. Target localization involves understanding natural language or image cues to pinpoint a destination on a map. Self-localization requires a robot to determine its precise position within a map, especially challenging in repetitive environments like warehouses where traditional methods often rely on artificial landmarks (e.g., QR codes). Path planning further divides into global planning for rough route generation and local planning for real-time obstacle avoidance and reaching intermediate waypoints.

While foundation models have shown promise in integrating smaller models to tackle broader tasks, the optimal number of models and their effective integration for comprehensive navigation remained an open question.

ByteDance’s Astra, detailed in their paper “Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning” (website: https://astra-mobility.github.io/), addresses these limitations. Following the System 1/System 2 paradigm, Astra features two primary sub-models: Astra-Global and Astra-Local. Astra-Global handles low-frequency tasks like target and self-localization, while Astra-Local manages high-frequency tasks such as local path planning and odometry estimation. This architecture promises to revolutionize how robots navigate complex indoor spaces.

Astra-Global: The Intelligent Brain for Global Localization

Astra-Global serves as the intelligent core of the Astra architecture, responsible for critical low-frequency tasks: self-localization and target localization. It functions as a Multimodal Large Language Model (MLLM), adept at processing both visual and linguistic inputs to achieve precise global positioning within a map. Its strength lies in utilizing a hybrid topological-semantic graph as contextual input, allowing the model to accurately locate positions based on query images or text prompts.

The construction of this robust localization system begins with offline mapping. The research team developed an offline method to build a hybrid topological-semantic graph G=(V,E,L):

• V (Nodes): Keyframes, obtained by temporal downsampling of input video and SfM-estimated 6-Degrees-of-Freedom (DoF) camera poses, act as nodes encoding camera poses and landmark references.
• E (Edges): Undirected edges establish connectivity based on relative node poses, crucial for global path planning.
• L (Landmarks): Semantic landmark information is extracted by Astra-Global from visual data at each node, enriching the map’s semantic understanding. These landmarks store semantic attributes and are connected to multiple nodes via co-visibility relationships.

In practical localization, Astra-Global’s self-localization and target localization capabilities leverage a coarse-to-fine two-stage process for visual-language localization. The coarse stage analyzes input images and localization prompts, detects landmarks, establishes correspondence with a pre-built landmark map, and filters candidates based on visual consistency. The fine stage then uses the query image and coarse output to sample reference map nodes from the offline map, comparing their visual and positional information to directly output the predicted pose.

For language-based target localization, the model interprets natural language instructions, identifies relevant landmarks using their functional descriptions within the map, and then leverages landmark-to-node association mechanisms to locate relevant nodes, retrieving target images and 6-DoF poses.

To empower Astra-Global with robust localization abilities, the team employed a meticulous training methodology. Using Qwen2.5-VL as the backbone, they combined Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). SFT involved diverse datasets for various tasks, including coarse and fine localization, co-visibility detection, and motion trend estimation. In the GRPO phase, a rule-based reward function (including format, landmark extraction, map matching, and extra landmark rewards) was used to train for visual-language localization. Experiments showed GRPO significantly improved Astra-Global’s zero-shot generalization, achieving 99.9% localization accuracy in unseen home environments, surpassing SFT-only methods.

Astra-Local: The Intelligent Assistant for Local Planning

Astra-Local acts as the intelligent assistant for Astra’s high-frequency tasks, a multi-task network capable of efficiently generating local paths and accurately estimating odometry from sensor data. Its architecture comprises three core components: a 4D spatio-temporal encoder, a planning head, and an odometry head.

The 4D spatio-temporal encoder replaces traditional mobile stack perception and prediction modules. It begins with a 3D spatial encoder that processes N omnidirectional images through a Vision Transformer (ViT) and Lift-Splat-Shoot to convert 2D image features into 3D voxel features. This 3D encoder is trained using self-supervised learning via 3D volumetric differentiable neural rendering. The 4D spatio-temporal encoder then builds upon the 3D encoder, taking past voxel features and future timestamps as input to predict future voxel features through ResNet and DiT modules, providing current and future environmental representations for planning and odometry.

The planning head, based on pre-trained 4D features, robot speed, and task information, generates executable trajectories using Transformer-based flow matching. To prevent collisions, the planning head incorporates a masked ESDF loss (Euclidean Signed Distance Field). This loss calculates the ESDF of a 3D occupancy map and applies a 2D ground truth trajectory mask, significantly reducing collision rates. Experiments demonstrate its superior performance in collision rate and overall score on out-of-distribution (OOD) datasets compared to other methods.

The odometry head predicts the robot’s relative pose using current and past 4D features and additional sensor data (e.g., IMU, wheel data). It trains a Transformer model to fuse information from different sensors. Each sensor modality is processed by a specific tokenizer, combined with modality embeddings and temporal positional embeddi…

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🔗 Sumber: syncedreview.com