Robotics & Physical AI

Skill2Real introduces an agentic framework for transferring robot manipulation skills through a shared API rather than task-specific policy retraining. Its hierarchical memories and Proposer–Verifier–Governor loop deliver strong results on simulated benchmarks and frozen real-world skills.

MotorMind is a robotics harness that connects a general-purpose vision-language model to deterministic robot control through mid-level actions and continuous feedback. The approach explores whether a VLM can perform zero-shot manipulation without task-specific policies, coding agents, or extra grounding tools.

PyRUA-Lean replaces repeated tool calls with executable Python cells that compose robot primitives, perform local checks and request only necessary observations. Across 700 simulated instances, it raised success from 63.1% to 71.7% under the same LLM-call budget while cutting input tokens by 65% on jointly solved tasks.

A remembered object location can become wrong while a robot is on its way to inspect it. EvolvingNav combines time-indexed 3D memory, probabilistic belief updates, and visual evidence to support navigation in evolving environments.

A new survey organizes robot in-context learning into four execution interfaces and examines how demonstrations can preserve task requirements as environments change. It also proposes a clearer way to evaluate teaching responsiveness, physical transfer, and experience reuse.

Researchers at the Hong Kong University of Science and Technology introduce WorldLine, an action-driven visual simulator for robotic manipulation. The system separates transferable robot–object dynamics learning from the grounding of heterogeneous robot controls.

RoboFoundry proposes a “system-as-policy” framework for embodied agents, treating context, memory, skills, and execution interfaces as a unified surface for improvement. The paper reports strong gains across embodied-task, long-horizon memory, and manipulation benchmarks.

EmbodiedMemory-Bench turns embodied memory into a measurable capability through executable, long-horizon interactions. It evaluates four challenges and introduces a structured external memory system together with the EMem-8B policy.

A new framework called Physical Coding treats robot state and execution as revisable programs rather than opaque action sequences. Its HexaAnything system uses tools, verification, and feedback to make physical agents more adaptable.

Researchers from UC Berkeley and collaborators introduce Morphometric Imitation, a three-stage pipeline that turns reconstructed human hand-object interactions into executable robot demonstrations and visuomotor policies. Across three robot hands and ten interaction tasks, the method reached a 89.3% zero-shot success rate in real-world trials.

World Action Agent places vision-language models inside an interactive visual action workspace, allowing robot actions to be previewed, edited, and corrected before and during execution. Its results suggest that a structured action loop can improve manipulation and transfer across environments.

Tesla is redirecting people and factory resources toward its Optimus humanoid robot, but difficult hand assembly, unreliable sensing, limited autonomy, and worker resistance are complicating its scale-up.

Skild AI has demonstrated a humanoid robot that learns to dribble, shield the ball, tackle and recover from falls through repeated matches against earlier versions of itself. The project points to a possible way of reducing reliance on manually designed robot skills and reward functions.

A new startup founded by former Huawei large-model and multimodal AI leaders is pursuing a Large Physics Model, or LPM. Its central hypothesis is that robots may gain reusable, cross-task abilities through large-scale pretraining, much as language models did with text.

Spatial-Interactor moves spatial reasoning beyond static visual questions by training vision-language models to track state changes caused by actions, object motion, and viewpoint shifts.

High task success does not necessarily mean that an embodied agent understood its instruction. RoboFollow shows how low-entropy scenes allow policies to rely on vision and default behavior while largely ignoring language.

The $11 million XPRIZE Wildfire competition demonstrated that satellites, ground sensors, and drones can identify a blaze within minutes. Fully autonomous suppression remains much harder.

CARE presents a data-centric framework for recovering robotic manipulation after execution drifts from the intended trajectory. It learns stage-specific failure patterns from real failed rollouts and uses them to generate corrective demonstrations and atomic recovery actions.

ShieldVLA brings Hamilton-Jacobi reachability into VLA safety fine-tuning, using visual observations to estimate whether a robot can still recover safely. The paper reports a 57% average reduction in cumulative safety cost across five navigation and manipulation benchmarks, alongside a 0.13 success-rate gain over SafeVLA.

HuRo introduces a robotization pipeline that converts heterogeneous human manipulation videos into robot-aligned observations and action trajectories. Its experiments show that scaling this data can improve real-world manipulation and out-of-distribution generalization.

A new study distills world-model representations into compact vision-language-action policies without putting future prediction inside the robot control loop. The approach improves manipulation results while preserving the baseline policy’s deployment cost.