RoboRSI is a robot self‑improvement system that uses Top‑Down Skill Refinement to decompose tasks into compound, atomic, and base skills with clear responsibilities and input‑output contracts. The system coordinates a Manager, Planner, Engineer, and Reviewer to plan, execute, diagnose, and validate skill revisions, allowing humans to guide objectives and corrections. On a mobile manipulator, RoboRSI successfully learns multi‑object household cleanup over 104 rounds and outperforms the strongest baseline by 2.7 to 11.0 percentage points on several simulation benchmarks.
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
arXiv:2610.35604v1 Announce Type: cross Abstract: Generalist robot policies and language-model agents provide complementary capabilities, but completing a task also requires adapting their use to the...
arXiv:2606. 19980v1 Announce Type: new Abstract: Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence.
The paper introduces URAI, a Universal Robot‑Agent Interface that separates robot control into two roles: a programming agent that writes reusable, task‑specific tools from intent, and an execution agent that calls these tools in a feedback loop. This design keeps high‑level decision making in the model while delegating low‑level motion to code, allowing tool revisions to persist across episodes without retraining the foundation model. Experiments on RoboDojo and AgileX tasks show significant gains in success rate, speed, and token efficiency compared to direct fingertip control and pre‑written programs.
Reconstruct, Practice, Go Real (RPG) is a framework that enables autonomous improvement of robot execution systems without updating model weights. RPG identifies manipulation capabilities from offline data, creates simulation practice tasks, and uses feedback to diagnose failures, develop new symbolic skills, refine existing ones, and revise the system prompt. Across 22 manipulation tasks, RPG raises task success from 28.6% after the first practice round to 95.0% after 15 rounds, outperforming baselines and achieving perfect success on 30 physical trials after calibration.
The paper introduces HALTER, a graph-based system that automates the reset and evaluation of long-horizon robot manipulation tasks. HALTER constructs a spatial scene graph from point clouds and vision models, uses an LLM to score rollouts, plan resets, and verify success, all without labeled success images. In experiments on a Franka arm, HALTER restores scenes in 76% of episodes, improves skill completion estimation, and reduces operator time by 72% compared to manual reset.
arXiv:2610.03476v1 Announce Type: cross Abstract: Long-horizon mobile manipulation presents significant challenges due to compounding execution errors and capacity interference between locomotion and...
arXiv:2610.08862v1 Announce Type: cross Abstract: Household robots must accommodate new user instructions while executing ongoing tasks. Existing agents often regenerate or extensively revise the rem...
arXiv:2604.07799v3 Announce Type: replace-cross Abstract: Robots deployed for long periods keep improving their skills, and each update changes a released system. We treat this as a software-lifecycl...
arXiv:2610.18792v1 Announce Type: cross Abstract: Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for...
OmniBrain-0 is a lightweight, unified embodied agent that coordinates manipulation and navigation through a shared high‑level decision model. It integrates task understanding, planning, progress tracking, and feedback‑driven recovery, and is trained via embodied pretraining, domain‑specialized agent distillation, and multi‑teacher on‑policy distillation. The framework also introduces Rubric‑Guided Recursive Self‑Improvement, a data flywheel that refines rubrics and trajectories for continual domain‑expert updates, enabling efficient transfer and consolidation of embodied capabilities across multiple benchmarks and real‑world tasks.
RoboAware learns to coordinate modular robot skills by training a state‑conditioned responsibility coordinator using counterfactual outcomes. It introduces State‑Locked Counterfactual Branching (SCB) to generate outcomes for all admissible skill families from the same state, and Execution‑Aware Learning (EAL) to distill these outcomes into family‑conditioned values via Monte Carlo tree search and Q‑learning. In single‑episode evaluations across 100 tasks, RoboAware achieves a 77.0% overall success rate, outperforming existing baselines on RoboSuite, LIBERO‑Pro, and RoboTwin bimanual tasks.