Robotics & Physical AI

SuperNav introduces an agentic navigation system for service robots that lets a pretrained multimodal model interpret requests and scenes while specialized navigation tools handle motion execution. The approach targets generalization across tasks and unfamiliar environments.

SimpleICL revisits a basic question in robot in-context learning: what should a robot actually learn from a visual demonstration? The work combines a clearer task definition with a minimal visual-prompt encoder and a low-cost data collection recipe.

UniWAM presents a unified world-action model that combines physical reasoning, visual world generation, and action prediction in one training framework. It brings together human egocentric videos, robot demonstrations, and visual question answering data to improve embodied understanding and control.

NVIDIA researchers introduce Long-WAM, a model-and-system framework that brings longer visual history to real-time robot control. Its key insight is that autoregressive video pretraining matters more than simply enlarging the context window.

RobotWorld is a simulation benchmark for testing whether multimodal agents can turn instructions, visual observations, and tool use into reliable robot behavior. Its results show that agents can build sophisticated perception and control pipelines, but still struggle to maintain state, recover from failure, and verify completion.

DiVeR identifies decision-critical states in robot trajectories by measuring the dispersion of sampled action representations. It uses that signal to focus verifier training where action selection has the greatest effect on task success.

A robot may execute a command differently from the motion predicted by a vision-language-action policy because of friction, backlash, payload changes, or wear. A new approach adapts the VLA during deployment by learning from this command-to-motion gap, without task labels or rewards.

EyeRobot 2.0 actively steers two eye viewpoints toward 3D fixation targets, concentrating visual computation where manipulation matters most. The approach explores precise bimanual control with a single stereo camera and no wrist-mounted cameras.

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.

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.

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.

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.

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.

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.