Robotics Papers of the Week

In this work we ask whether generalist robot policies are capable of multi-task, generalist improvement in deployment - targeting many diverse tasks simultaneously - and what algorithmic recipe enables this.

This study proposes a near-real-time UAV-based computer vision framework integrating YOLO-based object detection, geometric fire-source localization, and visible light communication (VLC).

We present Pixel, a co-designed full-stack system for learning general-purpose mobile manipulation.

Humanoid robots are typically trained as solitary agents, leaving them unable to coordinate physically with human partners who naturally anticipate and adjust to one another. To bridge this gap, we present Social Humanoid, a hierarchical system that generates and executes socially responsive whole-body motion on a 29-DoF Unitree G1 conditioned on a human partner's motion.

We propose Geometric Support Behavior Regularization (GSBR), which penalizes actions by their distance from the base policy's support.

We introduce ID-WM (Implicit Dynamics World Models), a hierarchical world model that decouples state-space reachability from local control.

We introduce VFlash, a method for fast, accurate visual planning through Flash Primitive Decoding.

In this work, we study embodied recursive self-improvement (RSI) through real-world exploration, code refinement, and deployment using PyRUA-REAL, a framework in which coding agents retain interaction experience as persistent memory and reusable executable skills that inform subsequent exploration and improvement.

We present Pixel-UMI, a pipeline for learning environment-aware whole-body policies from human data.

To democratize RUA research, we introduce uni-robot-agent, a unified harness for competitive open-weight robot use that supports further training.

We introduce DreamX, which repurposes a pretrained video diffusion model as a universal latent action learner.

We show that harness design can be automated, offering a strong alternative to finetuning on a novel setup.

For example, an agent that calls the VLA in the agentic loop can stall the fast execution, while a VLA that calls the agent on errors tends to re-trigger fallbacks repeatedly or completely miss silent failures. To address this, we propose REA (Routed Embodied Agent), a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain.

While post-training approaches can mitigate this through explicit training, they demand expensive real-world robotics data and require computationally intensive post-training for every individual base model. To resolve these limitations, we introduce D&A (Describe & Assess): our approach decouples progress estimation into an objective, task-grounded textual description of scene dynamics followed by an assessment phase using explicit rubrics.

This requires neither sufficient temporal dependence nor any assumptions on how the unobserved regimes evolve and holds even when the action-relevant latent variables are statistically dependent on the rest. We therefore propose CWAM (Causal WAM), which incorporates the constraint into a pretrained WAM by adding a sparse routing path to its action head, with the world modeling objective remaining unchanged.

A robot world model that serves as a simulator must do more than generate plausible videos: it must faithfully follow commanded actions, maintain consistency across camera views, and sustain coherent long-horizon rollouts. We present PrimeSim, an action-conditioned, multiview world model simulator that addresses these challenges through progressive midtraining and posttraining.

Egocentric human videos offer a scalable substitute, but overlaying rendered robots on human videos or retargeting human poses into robot actions leaves significant visual or action gaps for high-DoF robot embodiments. To address this limitation, we propose RoboTryOn, which treats human-to-robot translation as virtual try-on: try-on keeps the person and swaps the garment, RoboTryOn keeps the demonstrated motion and swaps the human for the robot.

We show that visual perturbations can instead alter the physical action while keeping the generated rationale word-for-word identical.

We propose \textbf{TAMPAgent}, an agentic planning framework that unifies the reasoning of LVLMs with the structured constraint satisfaction of TAMP systems.

We present SLA, a Spatial-Language-Action Model that scales implicit spatial alignment to continual pre-training and maintains it during downstream post-training.